| 10631749 | Computational Statistical Mechanics [PHYS-04/A] [ENG] | 1st | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
The course of Computational Statistical Mechanics aims to provide the necessary knowledge to understand and implement classical molecular dynamics and Monte Carlo techniques. The methods, that allow us to generate trajectories in phase space for sampling distinct statistical ensembles, will be studied. Some techniques which offer the possibility to calculate the free energy will be also discussed and it will be shown how the use of such results can provide a description of the atoms and molecules phase diagrams. At the end of the course, students will develop the ability of a quantitative reasoning and numerical skills useful for studying, modeling and understanding a large class of atomic and molecular systems as well as supramolecular aggregates. In addition, the student will be able to utilize the most common simulation packages which are available for a numerical study of complex systems, such as colloidal and bio-molecular systems, due to the acquired full knowledge of algorithms and numerical techniques on which these programs are built. Particular emphasis will be given to object-oriented and generic programming in the implementation of a computer simulation code. In particular, the modern C++ programming language will be introduced and discussed in the context of atomistic simulations. It will be also illustrated the use of the Python language, through the NumPy and MatPlotLib libraries, to analyze and visualize the data produced by computer simulations. During the course there will be also hands-on lectures, so that students will be able to put into practice the acquired knowledge through the implementation of their own simulation code. Students will be also stimulated to present the results obtained from the simulations, so as to test their ability to communicate clearly and effectively such results. The development of a numerical simulation code will be an opportunity for the students to design and develop their own project. This way they will be able to show their learning level and ability to apply independently the theoretical concepts acquired in the course.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1) Know common techniques to carry out computer simulations
OF 2) Know object oriented programming for scientific computations.
OF 3) Know common methods for analyzing data obtained from computer simulations.
OF 4) Understand data produced by computer simulations.
B - Application skills
OF 5) Ability to implement a simulation code.
OF 6) Ability to exploit simulations to obtain information about the physical properties of investigated systems.
OF 7) Be able to develop computer codes for analyzing data produced by computer simulations.
C - Autonomy of judgment
OF 8) Be able to critically analyze the results of “numerical experiments”.
OF 9) Be able to integrate autonomously the acquired knowledge in order to face new problems that require additional numeric techniques.
OF 10) Be able to identify the best technique to solve and study a physical problem numerically.
D - Communication skills
OF 11) Know how to communicate clearly to specialists and non-specialists, through manuscripts and presentations, the results obtained.
OF 12) Know how to clearly discuss a scientific topic.
OF 13) Know how to reproduce calculations related to a given scientific topic in a critical and informed manner.
E - Ability to learn
OF 14) Have the ability to learn new algorithms and numerical techniques by exploiting the scientific literature.
OF 15) Be able to conceive and develop their own project consisting of writing a simulation code or implementing a numerical technique.
OF 16) Be able to overcome difficulties and setbacks in the implementation of numerical techniques through original ideas and solutions.
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| 10631776 | Computational Solid State Physics [PHYS-04/A] [ENG] | 1st | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
The aim of the course 'Computational Solid State Physics' is to provide both theoretical and practical understanding with the two main numerical approaches currently in use for the solution of the quantum many body problem in condensed matter physics:
a) Density Functional Theory, which allows to obtain predictions from first principles of electronic states, structural energies, and interatomic forces in molecules and solids;
b) Quantum Monte Carlo methods - variational, diffusion, path-integral – which can be applied to the numerical study of various many-body quantum systems (liquid or solid helium, electron gas, electrons in atoms and molecules).
SPECIFIC OBJECTIVES:
A- Knowledge and Understanding:
OF1: To know and understand the fundamentals of Hartree-Fock (H-F) theory.
OF2: To know and understand the fundamentals of Density Functional Theory (DFT).
OF3: To know and understand the fundamentals of Pseudopotential theory (PPT).
OF4: To know and understand the DFT+PPT theory of crystalline systems.
OF5: To know and understand the variational Monte Carlo (MC) method for identical particles.
OF6: To know and understand the "projection MC" method for identical particles.
OF7: To know and understand the path integral Monte Carlo (PIMC) method.
OF8: To know and understand the "sign problem" for systems of many identical fermions.
B- Application Skills:
OF9: To apply DFT+PPT to simple solid-state systems (using software like Quantum Espresso).
OF10: To apply various quantum Monte Carlo methods to simple systems of many identical bosons or fermions (writing simple C codes and using large pre-existing FORTRAN codes).
C- Autonomy of Judgement:
OF11: To be able to assess, for a real quantum solid or fluid, which theories and algorithms presented in the course are suitable for describing and/or predicting which physical properties.
OF12: To be able to evaluate the feasibility, in terms of memory and CPU time, of a numerical project in molecular or solid-state physics.
D- Communication Skills:
OF13: To be able to present the results of a theoretical-numerical project.
OF14: To be able to write concise reports on the results of a theoretical-numerical project.
Ability to Learn:
OF15: To progress autonomously in C programming skills.
OF16: To progress autonomously in the use of existing software and codes.
OF17: To progress in graphical visualization skills of one's own results.
OF18: To progress in the ability to read reviews and research articles.
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| 10631775 | Computational Biophysics [PHYS-04/A] [ENG] | 1st | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
This course is designed as an introduction to computational biology and biophysics. It aims to bridge the gap between institutional learning and active research. The course is structured around three main aspects: i) TOPICS (principles, ideas); ii) METHODS (algorithms and computational techniques); iii) PERSPECTIVES of contemporary computational biology. Extensive reference and critical introductions to literature and current texts will be provided as guides for individual study. Efforts will be made to provide a clear framework of bibliographic references for each topic discussed, aiding in preparation for the final exam. At the end of the course, special guests will present original research lines of interest to students in biosystems, materials physics, and theoretical courses. By successfully completing the course, students will be able to navigate the world of computational biophysics at various scales (from molecules to cells) and master the main computation and analysis algorithms used in the field.
SPECIFIC OBJECTIVES:
A - Knowledge and Understanding
SO 1) Gain a historical-critical perspective of modern computational biology/biophysics
SO 2) Understand the fundamentals of modern evolutionary theory
SO 3) Gain practical experience with data analysis models based on Bayesian inference
SO 4) Gain direct experience with major bioinformatics databases (SwissProt, pFam, PDB,…)
B - Applied Skills
SO 7) Translate at least the main computational biophysics simulation and analysis algorithms into pseudo-code
SO 8) Improve programming skills in scripting languages (Python) or compiled languages (C/C++)
SO 9) Execute a molecular dynamics simulation of a small protein on GROMACS
C - Judgment Autonomy
SO 10) Evaluate the quality of a scientific article
D - Communication Skills
SO 11) Report the results of a research project to the class participants
SO 12) Actively participate in classroom discussions (in Italian and/or English)
E - Learning Skills
SO 13) Acquire fluency in consulting specific databases (e.g., PubMed, Google Scholar) to support/refute a research hypothesis
SO 14) Actively participate in the organization of self-learning groups
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| 10631781 | Advanced Mathematical Methods for Physics [PHYS-02/A] [ENG] | 1st | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
The main objective of Advanced Mathematical Methods for Physics is that of providing an introduction to up-to-date computational methods that are used in research areas of current interest. Three different courses are offered.
The goal of the third course is to provide the students with the theoretical background of perturbative and asymptotic analysis used in many fields of theoretical physics:
a) Definition and properties of the perturbative and asymptotic exapansions used in theoretical physics;
b) Introduction to some asymptiotic methods --- Boundary Layers, WKB, Multiple Scale, Renormalization Group --- and analisys of their filds of applicability.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1) To know and understand the foundations of the perturbative methods
OF 2) To know and understand the foundations asymptotic analysis
OF 3) To know and understand the Boundary Layer Theory
OF 4) To know and understand the WKB method
OF 5) To know and understand the Multiple Scale method
OF 6) To know and understand the Renormalization Group and it connections with asymptotica analysis.
B - Application skills
OF 7) Application of the asymptotic analysis to the solution of comlex problems
OF 8) Application of the Boundary Layer Theory, WKB method and Multiple Scale method to the study of simple problems.
OF 9) Application of the Renormalization Group method to the asymptotic analysis of the solution of simple ordinary differential equations.
C - Autonomy of judgment
OF 10) Ability to analize a simple perturbative problem.
OF 11) Ability to evaluate the structure of a simple perturbative problem and use the more appropriate method to its study.
D - Communication skills
OF 12) Ability to create an effective presentation of the results of a theoretical project
OF 13) Ability to present the basis of the asymptotic analysis and some of its methods.
E - Ability to learn
OF 14) Autonomous improvement in the study of perturbation method
OF 15) Autonomous improvement in the use of asymptotic analysis in more comlex problems
OF 16) Autonomous improvement in reading and understanding research articles and reviews
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| 10631966 | STATISTICAL INFERENCE AND BIOLOGICAL DATA ANALYSIS [PHYS-02/A] [ENG] | 1st | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
The course aims to introduce students to the fundamental concepts of statistical inference and its applications in modern biology, utilizing the language and methods of statistical physics and complexity science. By the end of the course, students should be able to master the theoretical concepts of information and entropy, and to apply analytical and computational approaches (via Python/Google Colab notebooks) to solve real-world problems related to the analysis of high-dimensional biological and biophysical data.
SPECIFIC OBJECTIVES:
A – Knowledge and Understanding
OF 1) To acquire knowledge of the fundamentals of Bayesian inference and asymptotic inference, and their deep connection to the principle of maximum entropy and statistical physics.
OF 2) To understand key concepts in information theory (Shannon entropy, mutual information, Fisher-Shannon information) and the mechanisms of phase transitions in high-dimensional inference.
OF 3) To develop the ability to comprehend the mathematical structure of statistical physics models and unsupervised/supervised learning models applied to biological systems (Boltzmann Machines, Ising Models, Hidden Markov Models – HMMs, and simple neural networks).
B – Applied Skills
OF 4) To be able to estimate physical and biological parameters (e.g., diffusion coefficients) based on sparse experimental data or single-particle tracking data.
OF 5) To be able to apply dimensionality reduction techniques (PCA and Sparse PCA) to identify structured patterns within complex biological data, such as the activity of neural populations.
OF 6) To be able to implement regularization strategies—including the use of priors and sparsity constraints—to prevent overfitting in the data-scarce regimes typical of biological research.
OF 7) To be able to utilize network inference and maximum entropy methods (Direct Coupling Analysis) to predict structural contacts in proteins, model molecular evolution, and generate artificial sequences.
OF 8) To be able to analyze time series and genomic data using Markov chains and Hidden Markov Models (HMMs) to study signal propagation and the organization of viral genomes.
C – Independent Judgment
OF 9) To be able to critically evaluate which statistical model or computational architecture is most suitable for describing a biological dataset, balancing model complexity against the quantity of available data (data-scarce regime).
OF 10) To be able to integrate methodologies from the statistical physics of disordered systems and information theory to interpret phenomena of emergent behavior and collective organization in living systems.
D – Communication Skills
OF 11) Be able to present the results of biophysical data analysis clearly, rigorously, and using appropriate interdisciplinary formalism, translating physical concepts (e.g., temperature, free energy) into biological or information-theoretic properties, and vice versa.
E – Learning Skills
OF 12) Possess the ability to independently consult advanced scientific textbooks, research monographs, and literature articles in the fields of applied statistical physics, quantitative biology, and physics-based machine learning.
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| 10631744 | SOFT AND BIOLOGICAL MATTER [PHYS-04/A] [ENG] | 1st | 1st | 6 |
Educational objectives GENERAL OBJECTIVES: The "Soft and Biological Matter" course aims to provide the necessary knowledge to understand the structure of soft and biological matter, in the relevant scales of
length and time. Important arguments include the origins of the effective forces between macromolecules, the aggregation processes which result in the formation of vesicles, micelles, membranes, the formation of gel phases, structural and dynamic properties of synthetic and biological (nucleic acids and proteins) polymers. At the end of the course, students will develop quantitative reasoning and analytical skills useful for studying, modelling and understanding phenomena related to the dynamic and structural properties of soft and biological matter.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1) To understand the physics of soft and biological matter
OF 2) To understand energetic and entropic forces
OF 3) To understand molecular aggregation
OF 4) To understand thermodynamic stability and critical phenomena in soft matter
B - Application skills
OF5) To be able to apply learned methods/techniques to novel problems
C - Autonomy of judgment
OF 6) To be able to apply the topic discussed in the course to the general context of soft and biological matter.
D - Communication skills
E - Ability to learn
OF 7) To be able to understand a scientific publication and deepen her/his own understanding of the arguments discussed in the course.
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| 10628727 | Group Theory in Mathematical Physics [MATH-04/A] [ENG] | 1st | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
The main goal of the course is to introduce students to the mathematical theory of groups (mainly: discrete groups and compact Lie groups) by a Mathematical Physics approach which emphasizes the role of representations of symmetries in terms of states or observables of the corresponding theory. Such an approach allows an immediate comparison between classical theories (Poisson brackets) and quantum theories (commutators).
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF1) To know the fundamental concepts in the theory of finite groups and matrix Lie groups, and in the theory of their linear, unitary or projective representations.
OF2) To know the mathematical structure of the Lie groups which more often appear in physical theories, and to understand the relation between such groups and the symmetries of the physical theory.
OF3) To understand the role of symmetries and Lie groups in (relativistic) field theories.
OF4) To understand the mathematical language of differential forms, and the reformulation of electromagnetism in such a language.
B – Application skills
OF 5) To be able to compute the commutation relations among the generators of the Lie algebra of a given (matrix) Lie group; to be able to explicitly compute such commutation relations in the most relevant cases: the rotation group, the Poincaré group, and the group SU(3).
OF 6) To be able to compute the tensor product of two representations of the rotation group, by using the Wigner Eckart theorem; to be able to interpret the result of such a computation in the application to compound systems (e.g. molecules).
OF7) To be able to determine whether a given differential form is closed and/or exact; to be able to translate the concepts concerning differential forms in the analogous concepts of vector analysis (gradient, curl or rotational, divergence) and vice versa.
C - Autonomy of judgment
OF 8) To be able to critically read an advanced book on symmetries in physics.
OF 9) To be able to integrate the knowledge acquired within the course, in order to apply them in the context of different physical theories, in connection e.g. with high energy physics or with condensed matter physics.
D – Communication skills
OF 10) Ability to discuss the symmetries of a physical system by appropriately using the language of differential forms and Lie groups.
E - Ability to learn
OF 11) Ability to read advanced monographies and research papers, which usually use the mathematical language of Lie groups and differential forms.
OF 12) Ability to "construct" a physical theory, by implementing in the theory the symmetries of the physical system under investigation, using Lie algebras and Lie groups as a fundamental tool.
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| 10631745 | THEORETICAL BIOPHYSICS [PHYS-02/A] [ENG] | 1st | 2nd | 6 |
Educational objectives GENERAL OBJECTIVES:
The main objective of the course in Theoretical Biophysics is to show how statistical physics has a crucial role for a quantitative understanding of many biological
phenomena. To this aim, the course focuses on two very general aspects present in a variety of biological processes: the role of noise and the signal to noise ratio; the
emergence of collective phenomena.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1) To acquire some fundamental background in statistical physics, related in particular to elementary stochastic processes, critical phenomena and statistical inference
OF 2) To learn the phenomenology of several important biological processes such as chemotaxis and chemoreception, photoreception, proteins, neural networks, living active matter and collective motion.
OF 3) to acquire modeling techniques
B - Application skills
OF 4) To be able to apply theoretical concepts and models to the quantitative description of the phenomenology experimentally characterized. To build models starting from the data.
C - Autonomy of judgment
OF 5) To be able to modify approaches derived from statistical physics to study specific phenomena occurring in biological systems.
D - Communication skills
E - Ability to learn
OF 6) Have the ability to consult and study scientific texts and literature of both theoretical and experimental character in a highly interdisciplinary context.
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| 10631756 | Non-equilibrium statistical mechanics [PHYS-02/A] [ENG] | 1st | 2nd | 6 |
Educational objectives GENERAL OBJECTIVES:
The goal of the course is the study of the foundations of the
statistical mechanics
of non equilibrium systems, with special enphasis on stochastic
models (e.q. Langevin equations)
i) to provide the student with a deep knowledge and understanding of
these
concepts, and
ii) to allow him (her) to successfully apply them in various physical
contexts. In particular, the student must be able to
use techniques of integration in the complex domain in all the
physical contexts in which they have applications.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
- To know the basis of the kinetic theory
- To understand how to use stochastic processes
B - Application skills
- To know the theory of fluctuations and the linear response
C - Autonomy of judgment
- To be able to integrate the knowledge acquired in order to apply it in
the more general context of statistical mechanics
E - Ability to learn
- To be able to read independently scientific texts and articles in
order to elaborate on the topics introduced in the course.
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| 10625970 | MATHEMATICAL PHYSICS [MATH-04/A] [ENG] | 1st | 2nd | 6 |
Educational objectives General Objectives:
To acquire knowledge of fundamental topics in Mathematical Physics and the related mathematical methods.
Specific Objectives:
Knowledge and Understanding:
By the end of the course, students will be familiar with the fundamentals of operator theory in Hilbert spaces, the mathematical structure of quantum mechanics, and the basic methods for studying problems in quantum mechanics from the perspective of mathematical physics.
Applying knowledge and understanding:
Students who pass the exam will be able to: i) apply the results of operator theory, ii) construct self-adjoint Hamiltonians and study their spectra, iii) rigorously approach a scattering problem, iv) understand the mathematical methods required for the study of the dynamics of many-body systems, with particular regard to the phenomenon of condensation.
Critical Thinking and Judgment:
Students who pass the exam will be able to recognize a mathematical-physical approach and analyze similarities and differences compared to the typical approach in Theoretical Physics.
Communication Skills:
Students who pass the exam will have developed the ability to communicate concepts, ideas, and methodologies in mathematical physics.
Learning Skills:
The knowledge acquired will enable students to pursue further study—either independently or through other courses—on more specialized aspects of the methods of mathematical physics.
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| 10631764 | NEURAL NETWORKS [PHYS-02/A] [ENG] | 1st | 2nd | 6 |
Educational objectives A - Knowledge and understanding
OF 1) Starting from the study of neurobiology of the nervous system, the student will first concentrate on the mechanisms regulating the electro-chemical properties of nerve cells and their connections, eventually studing the dynamics of populations of neuronal networks. The knowledge acquired will be on nonlinear and statistical physics compared to experimental data.
OF 2) The students will develop generally applicable skills in the field of theoretical physics of the complex systems and the nonlinear dynamics.
B - Application skills
OF 3) The student will be able to understand the dynamics of neuronal populations at the basis of the cognitive functions like decision making and short-term memory.
OF 4) The student will be able to apply analysis techniques and methods to electrophysiological data.
C - Autonomy of judgment
OF 5) By attending the lessons and with the regular interaction during the lessons themselves, the student will develop adequate autonomy of judgment, as he/she will be able to interface constantly with the teacher and critically analyze the information learned.
D - Communication skills
OF 6) The skills on the neurobiology of the nervous system will allow the student to interact with environments different from physics, enabling him/her to initiate multidisciplinary interactions in the life sciences.
E - Ability to learn
OF 7) The student will have the ability to evaluate and solve various problems of both data analysis and physics of complex systems.
OF 8) The acquired knowledge will allow the student to tackle the study of interdisciplinary papers on the physical phenomena underlying the behavior of the nervous system.
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| 10631740 | CONDENSED MATTER PHYSICS II [PHYS-04/A] [ENG] | 1st | 2nd | 6 |
Educational objectives GENERAL OBJECTIVES:
The course introduces the students to Condensed Matter phenomena related to the interaction between electrons, and of electrons with external electromagentic fields.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1 The Condensed Matter Physics II course provides a theoretical introduction students to the main methods and phenomena of condensed matter physics, related to electron-electron interactions.
The course will also feature selected examples of the applications of condensed matter theory methods to real-world research problems.
B - Application skills
OF 2: Theory lectures will be integrated by practical (analytical and numerical) exercises, addressing real-world problems.
C - Autonomy of judgment
OF3: After attending course, students will have developed quantitative and qualitative problem-solving skills related to condensed matter theory, which will allow them to understand and model fundamental phenomena in condensed matter.
D - Communication skills
E - Ability to learn
OF 6) To be able to read independently scientific texts and articles in order to elaborate on the topics introduced in the course.
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| 10631739 | PHYSICS OF LIQUIDS [PHYS-04/A] [ENG] | 1st | 2nd | 6 |
Educational objectives GENERAL OBJECTIVES:
The course in Physics of Liquids aims to provide the necessary knowledge
to understand the disordered state of matter. Emphasis will be directed toward the
connection between the inter-particle interaction potential and the resulting
equilibrium structure. The themes of short-range ordering
and of the dynamics in the fluid and glass phases
will be studied in depth. At the end of the course, students will develop quantitative reasoning skills and analytical abilities useful for studying, modelling and understanding phenomena related to disordered soft matter.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1) To know the theory of classical fluids, from mean field models, to integral theories and perturbative approaches.
OF 2) To understand the physical basis of the integral closures.
OF 3) To know how to extract structural and dynamical quantities from the scattering of X rays and neutrons.
OF 4) Know how to go from a microscopic theory to a hydrodynamic theory.
B - Application skills
OF 5) To be able to compute the cluster integrals that compose the virial coefficient for simple interaction potentials.
OF 6) To be able to solve the equations governing the structure of a fluid in the presence of external fields.
OF 7) To be able to apply perturbative techniques
…
C - Autonomy of judgment
OF 8) To be able to understand the results of experiments and simualtions on simple and complex liquids.
OF 9) To be able to integrate the knowledge acquired in order to choose the best closure relations for a particular problem.
D - Communication skills
OF 10) To know how to communicate the results of experiments and simulations on simple liquids.
E - Ability to learn
OF 11) Have the ability to consult and understand books and articles in order to gain a deeper knowledge of the topics discussed during the course.
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| 10631747 | BIOPHYSICS [PHYS-03/A] [ENG] | 1st | 2nd | 6 |
Educational objectives GENERAL OBJECTIVES
The bacterial cell occupies the same special place in biological physics as the hydrogen atom does in condensed matter physics, and for the same reasons. Bacteria are the first "atoms" of life to appear in the known Universe, and everything fundamental in life is found in bacteria, in its simplest forms. The aim of the course is to investigate some fundamental aspects of living systems in a journey that starts from the internal mechanisms by which the bacterial cell "thinks" and acts, passing through how the individual cell moves in the external physical environment and ending with the study of the collective behaviour of bacterial colonies.
All topics covered in the course are based on recent literature and discuss both experimental aspects and theoretical modelling.
SPECIFIC OBJECTIVES
A - Knowledge and understanding
OF 1) To know and understand the fundamentals of gene regulation in prokaryotes and the dynamics of transcriptional networks.
OF 2) To know and understand the fundamentals of low Reynolds number fluid dynamics.
OF 3) To know and understand the main manifestations of the out of equilibrium nature of active matter.
B - Application skills
OF 4) To be able to discuss the dynamical behaviour of a transcriptional network.
OF 5) Tp be able to solve some elementary problems of low Reynolds hydrodynamics.
OF 6) Tp be able to model the stochastic dynamics of active particle systems.
OF 7) To be able to describe with continuous models the growth of bacterial colonies.
C - Autonomy of judgement
OF 8) Using the knowledge acquired, the student will be able to formulate new models capable of describing situations not covered in the course.
D - Communication skills
OF 9) To know how to communicate in written reports an advanced concept.
OF 10) To be able to present a recent line of research in biophysics.
E - Ability to learn
OF 11) To be able to read independently scientific texts and articles in order to elaborate on the topics introduced in the course.
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| 10631735 | Nonlinear Waves and Solitons [PHYS-04/A] [ENG] | 1st | 2nd | 6 |
Educational objectives Formative targets:
The objectives of the course are to bring the student to a deep knowledge and understanding of the basic mathematical properties i) of the nonlinear wave propagation with or without dispersion or dissipation; ii) of the construction of nonlinear mathematical models of physical interest, through the multiscale method, like the soliton equations, and of the mathematical techniques to solve them, arriving to the introduction of current research topics in the theory of solitons and anomalous waves. At the end of the course the student must be able i) to apply the acquired methods to problems in nonlinear physics even different from those studied in the course, in fluid dynamics, nonlinear optics, theory of gravitation, etc .., solving typical problems of the nonlinear dynamics; ii) to integrate in autonomy the acquired knowledges through the suggested literature, to solve also problems of interest for him/her, and not investigated in the course. The student will have the ability to consult supplementary material, interesting scientific papers, having acquired the right knowledges and critical skill to evaluate their content and their potential benefits to his/her scientific interests. At last the student must be able to conceive and develop a research project in autonomy. In order to achieve these goals, we plan to involve the student, during the theoretical lectures and exercises, through general and specific questions related to the subject; or through the presentation in depth of some specific subject agreed with the teacher.
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| 10631691 | ADVANCED MACHINE LEARNING FOR PHYSICS [PHYS-01/A] [ENG] | 1st | 2nd | 6 |
Educational objectives GENERAL OBJECTIVES:
Acquire familiarity with advanced deep learning techniques based on differentiable neural network models with supervised, unsupervised and reinforced learning paradigms; acquire skills in modelling complex problems through deep learning techniques, and be able to apply them to different application contexts in the fields of physics and basic and applied scientific research.
Discussed topics include: general machine learning concepts, differentiable neural networks, regularization techniques. Convolutional neural network, neural network for sequence analysis (RNN, LSTM / GRU, Transformers). Advanced learning techniques: transfer learning, domain adaptation, adversarial learning, self-supervised and contrastive learning, model distillation.
Graph Neural Networks (static and dynamic) and application to structured models for physics: dynamic models, simulation of complex fluids, GNN Hamiltonians and Lagrangians. Generative and variational models: variational mean-field theory, expectation maximization, energy based and maximum entropy models (Hopfield networks, Boltzman machines and RBM), AutoEncoders, Variational AutoEncoders, GANs, Autoregressive flow models, invertible networks, generative models based on GNN. Quantum Neural Networks.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1) Knowledge of the functioning of neural networks and their mathematical interpretation as universal approximators
OF 2) Understanding of the limits and potential of advanced machine learning models
OF 3) Understanding of the limits and potential of DL in solving physics problems
B - Application skills
OF 4) Design, implementation, commissioning and analysis of deep learning architectures to solve complex problems in physics and scientific research.
C - Autonomy of judgment
OF 5) To be able to evaluate the performance of different architectures, and to evaluate the generalization capacity of the same
D - Communication skills
OF 6) Being able to clearly communicate the formulation of an advanced learning problem and its implementation, its applicability in realistic contexts
OF 7) Being able to motivate and to evaluate the generalization capacity of a DL model
E - Ability to learn
OF 8) Being able to learn alternative and more complex techniques
OF 9) Being able to implement existing techniques in an efficient, robust and reliable manner
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| 10631690 | PHYSICS OF COMPLEX SYSTEMS [PHYS-04/A] [ENG] | 1st | 2nd | 6 |
Educational objectives A - Knowledge and understanding
OF 1) To possess a basic knowledge of complexity science, i.e. the collective properties that emerge with a large number of interacting components (atoms, particles or bacteria in a physical or biological context, or people, machines or businesses in a socio-economic context).
OF 2) Understanding the mechanisms underlying the emergence of complex macroscopic properties from knowledge of microscopic mechanisms.
OF 3) Mastering the basic toolbox of a complexity scientist: information theory, network theory, scale invariance and critical phenomena, properties of dynamical systems, agent models.
B - Application skills
OF 4) Knowing how to devise simple models for complex phenomenologies.
OF 5) Being able to tackle complex problems analytically or computationally, translating research questions into concrete solution and verification actions.
OF 6) Being able to apply the techniques and methods learnt also outside the areas covered in the course.
OF 7) Integrating the knowledge acquired in order to formalise problems and obtain results and predictions of increasing accuracy.
C - Autonomy of judgment
OF 8) Being able to analyse phenomena, also through the acquisition of data and evidence, that fall within the scope of complexity and identify their essential elements.
OF 9) Being able to synthesise phenomenologies in order to be able to distill relevant and relevant questions.
OF 10) Being able to identify interesting new research directions.
D - Communication skills
OF 11) Being able to communicate complex issues in a simple way, focusing on the essential elements and revealing cause-effect relationships as far as possible.
OF 12) Being able to organise a coherent, profound yet comprehensible presentation.
OF 13) Knowing how to express one's thoughts in a way that stimulates group work and interaction with colleagues.
E - Ability to learn
OF 14) Have the ability to consult reference texts and articles.
OF 15) Being able to assess the relevance of results, their place in the scientific panorama of reference and their potential importance for the research topics of interest.
OF 16) Being able to design and develop a research project, identifying the main objectives and the possible paths to reach them.
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| 10631754 | Theory of Stochastic Processes [PHYS-02/A] [ENG] | 2nd | 1st | 6 |
Educational objectives A - Knowledge and understanding
OF 1) To know the fundamentals of the theory of stochastic processes, discrete and continuous, and thier formal framework in terms of resolution of Chapman-Kolmogorov, Fokker-Planck and master equations.
OF 2) To understand the similarities with the properties of equations already known to the students (like Schroedinger equation) and to learn equation resolution methods using operational calculus.
OF 3) To know the formalism of stochastic integration of stochastic differential equations and the connection to the Fokker-Planck partial differential equation.
B - Application skills
OF 4) To deduce physical properties of systems from the analysis of the stochastic equations.
OF 5) To apply newly learned methods to the estimate of first passage times and to the consequences of Arrhenius law on relaxation towards equilibrium in systems with rough potential landscapes.
OF 6) To apply methods and techniques to systems of different nature at and off equilibrium (viscous liquids, wave systmes, glassy systems, lasers).
C - Autonomy of judgment
OF 7) To be able to integrate acquired knwoledge and apply it also to cases not explicitly treated in the course.
OF 8) To be able to connect acquired knowledge to previous one, formalizing known concepts and connetcing them to more complex cases.
D - Communication skills
OF 9) To know how to orally present a demonstration procedure or an application assessing the most relevant and clarifying steps and their meaning.
E - Ability to learn
OF 10) To be able to consult diferrent textbooks and scientifc papers to the aim of autonomously deepening some of the arguments covered by the course.
OF 11) To be able to evaluate the effectiveness of the various studied approaches in relation to the treated problems.
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| 10631692 | STATISTICAL MECHANICS OF DISORDERED SYSTEMS [PHYS-02/A] [ENG] | 2nd | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
The main objective of the course is to illustrate the characteristics of some of the best known disordered models and to introduce the approximations and analytical techniques that allow their study in statistical mechanics.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1) To know the main disordered models, such as dilute ferromagnets, ferromagnets with random external field, and spin glasses
OF 2) To understand the different physical behaviors that arise as a result of the introduction of quenched disorder (slowing down of the dynamics, metastability, presence of many thermodynamic states).
OF 3) To know the main techniques of statistical mechanics (mean-field approximations, replica and cavity methods) that allow the analytical study of models with disorder.
B - Application skills
OF 4) To know how to apply an analytical technique (mean-field approximation, replica and cavity method) to a given Hamiltonian to study its physical behavior.
C - Autonomy of judgment
OF 5) Be able to recognize to which class of disordered systems a given Hamiltonian belongs.
D - Communication skills
OF 6) Ability to present the course topics orally in a non-technical language that allows understanding even by those who have not yet taken the course.
E - Ability to learn
OF 7) To be able to read scientific texts and articles in order to independently investigate the topics introduced during the course.
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| 10631778 | Statistical Physics and Machine Learning [PHYS-02/A] [ENG] | 2nd | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
The course is an advanced module aimed at guiding the students through a journey at the boundary between statistical physics and machine learning by introducing advanced concepts of equilibrium and out of equilibrium statistical mechanics and by illustrating their applications to learning models and development of artificial intelligence.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1) To acquire the main methods of statistical mechanics, probability and information theory relevant to applications in machine learning and inference, such as the replica approach, message passing, mutual information, data compression, Bayesian approaches
OF 2) To understand the different physical behaviour shown by inference and artificial learning procedures (curse of dimensionality, metastability, presence of multiple thermodynamic states)
B - Application skills
OF 3) To know how to apply an analytical technique to a given inference or learning setting to study its physical behavior
C - Autonomy of judgment
OF 4) Be able to recognize to which class of disordered systems a given inference or learning setting belongs
D - Communication skills
OF 5) Ability to learn from oral presentation of research results on topics similar to those introduced during the course
OF 6) Ability to present the course topics orally in a non-technical language that allows understanding even by those who have not yet taken the course
E - Ability to learn
OF 7) To be able to read scientific texts and articles in order to independently investigate the topics introduced during the course
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| 10631782 | Mathematical Statistical Field Theory [PHYS-02/A] [ENG] | 2nd | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
The course provides an introduction to the mathematical physics approach to
Statistical Field Theory, based in particular on bosonic and fermionic functional integrals, the theory of Renormalization
and the Wilsonian Renormalization Group. Such tools provide an unifying conceptual framework and a common language for several physical problems, charapterized by infinitely many interacting degrees of freedom and ranging from quantum or classical statistical physics to high energy physics. Solvable models will be also treated via exact methods, like bosonization or fermionization.
We will focus in particular: on phase transitions, universality and critical behavior in statistical mechanics models like Ising o Phi4; on the ultraviolet high energy problem posed by
Euclidean Quantum Field Theory models and their non perturbative construction with finite or infinite lattice cut-off, including the role of Ward Identities and chiral anomalies; on condensed matter systems with an emerging field theory description, and in particular on the universality of transport coefficients in graphene or topological insulators.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
1)To know and understand the rigorous results on
Phase transition, universality and critical phenomena in systems like the Ising, phi4 or dimer models, in solvable or non solvable cases and in cases with gaussian or non trivial fixed points.
2)To know and understand the constructive Euclidean approach to Quantum Field Theory, including the theory of renormalization, the non-perturbative construction of low dimensional models like Thirring or Gross Neveu models and the infrared limit in higher dimensions, the mathematical theory of chiral anomalies and their non-renormalization.
3)To know and understand the theory of universality of transport cofficients in condensed matter systems like Graphene, Luttinger liquids or topological insulators in presence of weak interactions.
B – Application skills
4) To be able to prove rigorous mathematical physics results
like the existence of the thermodynamic limit or to exacty compute physical observables in solvable models like 2d Ising or dimer models.
5)To be able to apply rigorous Renormalization Group methods to physical systems, writing observables in terms of expansions in the running coupling constants, to prove the renormalizability at any order and derive order by order bounds, and when possible
To extract non-perturbative informations in suitable regions of the parameters.
6) To be able to implement cancellations due to exact or emerging symmetries, to control the irrelevant terms, to derive Ward Identities and the beta function equations.
C - Autonomy of judgment
7) To be able to distinguish in the literature between conjectures, approximations and rigorous statements.
D –Communication skilll
8) Ability to explain, using the unifying language provided by the Renormalization Group ,different physical phenomana, showing the connections provided by the universal underlying structure.
E - Ability to learn
9) Ability of Critical reading, understanding and reproducing results in the modern mathematical and theoretical physics literature.
10)Use of rigorous methods to prove established but open conjectures.
11) Ability to apply the mathematical physics tools to interesting and open physical problems requiring advanced mathematics.
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| 10627540 | Mathematical models for neural networks [MATH-04/A] [ENG] | 2nd | 1st | 6 |
Educational objectives General objectives
Acquiring basic knowledge on the mathematical methods used in artificial intelligence modeling, with particular attention to "machine learning".
Specific objectives
Knowledge and understanding: at the end of the course the student will have knowledge of the basic notions and results (mainly in the areas of stochastic processes and statistical mechanics) used in the study of the main models of neural networks (e.g., Hopfield networks, Boltzmann machines, feed-forward networks).
Apply knowledge and understanding: the student will be able to identify the optimal architecture for a certain task and to solve the resulting model by determining a phase diagram; the student will have the basis to independently develop algorithms for learning and retrieval.
Critical and judgmental skills: the student will be able to determine the parameters that control the qualitative behaviour of a neural network and to estimate the values of these parameters that allow a good performance of the network; she/he will also be able to investigate the analogies and relationships between the topics covered during the course and during courses dedicated to statistics and data analysis.
Communication skills: ability to expose the contents in the oral and written part of the verification, possibly by means of presentations.
Learning skills: the knowledge acquired will allow a study, individual or taught in a LM course, related to more specialised aspects of statistical mechanics, development of algorithms, usage of big data.
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| 10631773 | SUPERCONDUCTIVITY AND SUPERFLUIDITY [PHYS-04/A] [ENG] | 2nd | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
The course aims to introduce the foundations of Superconductivity and Superfluidity. A preliminary part will be devoted to the phenomenological London and Ginzburg-Landau theories. The latter will be used to introduce the more general topic of spontaneous symmetry breaking in second-order phase transition, and the Anderson-Higgs mechanism for superconductivity. After discussion of the second-quantization for many-body fermionic and bosonic systems the focus will be on the microscopic models for superconductors (BCS Bardeen_Cooper e Schrieffer theory) and superfluids.
The final part will consist in a brief overview of current research topics on unconventional superconductors.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1) To know the basis of the superconducting phenomenon, its phenomenological and microscopic description and its experimental applications
OF 2) To understand key concepts as spontaneous symmetry breaking and order parametr for a phase transition, with particular emphasis on continous symmetries.
OF 3) To know basic applications of second quantization to fermionic and bosonic many-particle systems
B - Application skills
OF 4) To be able to describe the superfluid phenomen both for fermions and bosons, and its theoretical and experimental implications
C - Autonomy of judgment
OF 5) To be able to integrate the knowledge acquired in order to apply in the more general context of unconventional superconduvtivity and interacting fermionic systems
E - Ability to learn
OF 6) Have the ability to read scientific papers in order to further explore some of the topics introduced during the course.
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| 10631746 | MANY BODY PHYSICS [PHYS-04/A] [ENG] | 2nd | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
The aim of the course is to teach the main paradigms in many-body systems, particularly of fermionic systems, like electrons in metals, and to give an introduction to the methods of field theory in conndensed matter. At the end of the course the student should have acquired both technical competences (second quantization, Green function and Feynman diagrams at T=0 and T>0, response functions) and the physical understanding of the simplest approximations used to describe the many-body effects. In general the student should be able to understand both the language and the issues of modern research in correlated systems.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1) Basic concepts of Landau Fermi liquid theory, the fundamental paradigm of the metallic state
OF 2) Properties of the Green functions and their physical meaning
OF 3) Interaction representation. S matrix. Wick's theorem and Feynman diagrams.
OF 4) Self-energy and Dyson equation. Hartree-Fock approximation, RPA approximation
OF 5) Linear response theory; response function. Analytic properties. Reactive and absorptive part.
OF 6) Kramers-Kronig relations. Kubo formula Fluctuation and dissipation theorem
B - Application skills
OF 7) The second objective is to prepare the students to actively solve problems in physics where MB theory concepts are required. This will happen at first with problems structured within a conceptual scheme similar to the ones discussed and applied during the course. However, as their preparation progresses, students are also expected to use MB concepts for solving new problems in different applications.
C - Autonomy of judgment
OF 8) The third and more ambitious objective is to teach the students to think using concepts and methods from MB theory as a powerful problem solving tool in physics.
D - Communication skills
OF 9) Besides having a clear understanding of the new acquired concepts in MB theory, the students should correspondingly acquire the ability to communicate and transmit these concepts in a clear and direct way.
E - Ability to learn
OF 10) The students should become able to read and understand scientific books and articles where MB concepts are involved and should be able to deepen autonomously their knowledge in this field.
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| 10631737 | QUANTUM FIELD THEORY [PHYS-02/A] [ENG] | 2nd | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
This course will introduce the student to the most important concepts, ideas and tools of quantum field theory which has become the universal framework to describe all fundamental forces in nature. The student will understand how to construct field theories, quantize them in the presence of interactions and how to apply advanced techniques of regularization and renormalisation. Some special regard will be devoted to functional methods. The course will include the mathematical structure of non-Abelian gauge theories and their role in our present understanding of fundamental forces. The student will also have an elementary understanding of anomalies and their physical consequences in nature.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
The goal is for students to develop a critical understanding of the topics covered during the course, both as regards the purely theoretical aspects and in relation to the applications to different physical phenomena, and that they develop an adequate knowledge of the methods applied in theoretical physics, with particular reference to the methods usually used to conduct research in this sector.
B - Application skills
Alongside understanding the topics and methods used during the lessons, one of the objectives of the course is to enable students to apply those same methods to new problems, be they study or research.
C - Autonomy of judgment
One of the main objectives of the course is for students to develop critical skills with respect to the topics covered. They are often encouraged to follow other paths (than those followed during the lectures) for the achievement of results, or to propose interpretations or readings different from those presented by the teacher of the same results. Often during the lessons students are asked to make suggestions or make estimates in relation to specific calculations, with the aim of encouraging their autonomy of thought and their ability to make choices when confronted with delicate steps.
D - Communication skills
The course aims to increase students' communication skills, providing them with methodological tools that allow them to improve their ability to discuss in an original way topics related to theoretical and applicative aspects of quantum field theory.
E - Ability to learn
One of the most important objectives of the course is to provide students with a methodology that allows them to have access to a continuous updating of knowledge, trying in particular to increase their ability to deal with specialized literature.
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| 10631751 | QUANTUM INFORMATION AND COMPUTATION [PHYS-03/A] [ENG] | 2nd | 1st | 6 |
Educational objectives GENERAL OBJECTIVES:
This course will introduce students to the theory of classical and quantum information; elements of the algorithmic complexity theory; quantum computation and simulation; quantum cryptography. The student will study different experimental platforms to implement the protocols previously introduced.
At the end of the course, the student will be able, with a critical and analytical spirit, to formalize and analyze protocols of quantum communication and quantum computation. The ability to translate a quantum information processing task into an experimental platform will be developed, identifying its strengths and weaknesses.
SPECIFIC OBJECTIVES:
A - Knowledge and understanding
OF 1)To understand the fundamentals of information theory
OF 2) To understand the theory of quantum information
OF 3) To understand the language of quantum technologues
B - Application skills
OF 4) To be able to derive the evolution of a quantum circuit
OF 5) To be able to derive the evolution of an open quantum system
OF 6) To be able to model the different sources of noise present in a quantum information protocol
OF 7) To be able to define how to experimentally realize a quantum communication protocol
C - Autonomy of judgment
OF 8) To be able to exploit the knowledge acquired in quantum information for the implementation with different quantum technologies
D - Communication skills
OF 9) To know how to communicate in written reports an advanced concept
OF 10) To know how to present a recent research activity in the framework of quantum technologies
E - Ability to learn
OF 11) To be able to read independently scientific texts and articles in order to elaborate on the topics introduced in the course.
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| 10631741 | PHYSICS OF SOLIDS [PHYS-04/A] [ENG] | 2nd | 1st | 6 |
Educational objectives To form the students on the following topics:
- linear response theory in solids
- light-matter interaction: quantum description of optical and infrared
spectroscopies
- impact of electron electron interaction on excitations: plasmon and
excitons
- charge transport in solids
- topological properties of solids
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