{"id":943,"date":"2017-05-31T08:24:02","date_gmt":"2017-05-31T08:24:02","guid":{"rendered":"http:\/\/physics-complex-systems.fr\/?p=943"},"modified":"2026-05-27T13:38:43","modified_gmt":"2026-05-27T13:38:43","slug":"computational-science","status":"publish","type":"post","link":"https:\/\/physics-complex-systems.fr\/en\/computational-science.html","title":{"rendered":"Computational science"},"content":{"rendered":"

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Statistical learning and modeling of complex systems<\/h5>\n

Summary of the course:<\/b><\/u><\/p>\n

Complex systems formed by a large number of degrees of freedom can display a surprisingly rich set of different metastable structures, connected among them by time evolution with a hierarchy of timescales. Despite the variety of transformation phenomena observed in physics, chemistry, and biology – including phase transitions, reactions and protein folding – statistical mechanics and machine learning provide today very general frameworks to study them all. This course introduces computational methods for the analysis, modeling and simulation of complex systems. At the same time, modern neural networks are themselves complex systems made of many interacting degrees of freedom, exhibiting collective phenomena and phase transitions in how they learn, represent, and generate data. The same statistical-mechanics tools used to describe physical and biological systems are useful to illuminate the inner workings of these models.<\/p>\n

The first part focuses on statistical inference and machine learning, with emphasis on Bayesian inference, information theory, high-dimensional statistics, maximum-entropy models, energy-based models, regression, representation learning, autoencoders, variational autoencoders, diffusion models and transformers. Several of these models \u2014 notably energy-based models and Boltzmann machines \u2014 are inspired by statistical-mechanics systems, and the course examines the learning process itself through the lens of phase transitions, as in the retarded learning transition in PCA. The lectures are complemented by practical sessions in Python, including applications to generative modeling and dimensionality reduction.<\/p>\n

The second part of the course develops simulation algorithms to generate atomic trajectories of complex systems, together with data-driven approaches to learn stochastic models from the trajectories. Topics include molecular dynamics and Monte Carlo simulations, kinetic theories, order parameters, free-energy landscapes, Langevin models, and master equations. Practical computer sessions will allow the students to apply the theoretical methods on realistic trajectories of systems formed by thousands of atoms, investigating phenomena like protein folding and crystal nucleation.<\/p>\n

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Jorge Fernandez-de-Cossio-Diaz<\/th>\nFabio Pietrucci<\/th>\n<\/tr>\n
IPhT, CEA<\/td>\nSorbonne Universit\u00e9<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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First part : Statistical inference and machine learning<\/h6>\n
Lectures:<\/strong><\/div>\n
\u2022 Introduction. Bayesian inference. Estimators.
\n\u2022 Information theory: Entropy, KL divergence, Mutual information
\n\u2022 High-dimensional statistics, asymptotic inference. Retarded learning phase transition in PCA.
\n\u2022 Maximum Entropy principle. Energy based models: Boltzmann machines.
\n\u2022 Restricted Boltzmann machines. Representation learning. Relation between RBMs, Hopfield models, and PCA.
\n\u2022 Linear regression, regularization, variance-bias decomposition.
\n\u2022 Autoencoders. How autoencoders implement PCA. Variational autoencoders.
\n\u2022 Modern generative architectures: diffusion models & transformers
\nPractical sessions:<\/strong>
\n\u2022 Intro to Pytorch, Autoencoders, Variational Autoencoders, Ensemble methods, Dimensional reduction, Clustering, Classifiers.<\/div>\n

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Second part : Simulation methods and dynamical models<\/h6>\n

Lectures:<\/strong>
\n\u2022 Trajectories of complex systems (biomolecules, nanostructure complexes, solutions): local equilibrium, hierarchy of timescales, barriers.
\n\u2022 Simulation algorithms: molecular dynamics, Metropolis Monte Carlo, enhanced sampling.
\n\u2022 Projection on order parameters: metastability, relaxation as entropy production, correlation functions, free-energy landscapes, kinetic rate theories.
\n\u2022 Analytical forms and intuitive meaning of order parameters in different fields.
\n\u2022 Accuracy of Langevin models of projected trajectories: the meaning of friction and noise, Markovian and overdamped approximations, reading noise in the original trajectory.
\n\u2022 Parametrization of Langevin models: Kramers-Moyal vs likelihood maximization, which type of data and how much do we need?
\n\u2022 Machine-learning the optimal order parameter: committor function, kinetic variational principle, entropic variational principle.
\n\u2022 Parametrization of master equations: partitioning configurations via binning or clustering, meaning of the main eigenvalues and eigenvectors of the transition matrix, network of kinetic rates connecting metastable states.
\n\u2022 Introduction to a set of articles for the bibliographic project.
\nPractical sessions:<\/strong>
\n\u2022 Association and dissociation of complexes in solution, dynamics of homopolymer chains, folding of the Trp-cage protein, crystal nucleation from the liquid, chemical reactions in solution.[\/vc_column_text][\/vc_column][\/vc_row][vc_row css=”.vc_custom_1496785510591{margin-top: 20px !important;}”][vc_column][vc_column_text]<\/p>\n

Keywords : information theory<\/span> machine learning<\/span> Monte-Carlo<\/span> statistics<\/span> algorithms<\/span><\/div>\n

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