Probabilistic sampling for physics: finding needles in a field of high-dimensional haystacks

Session

Result Communication

Sep 7, 2023, 11:30 AM

Institut Pascal

28. Stochastic models form enhanced sampling in Chemistry

Léon Huet (IMPMC - Sorbonne Université)

9/7/23, 11:30 AM

The study of chemical reactions involving covalent links and breaks inherently presents stochastic complexities. To overcome the challenges posed by large energy barriers, researchers commonly use enhanced sampling techniques like transition path sampling and umbrella sampling to collect relevant data at the DFT level. However, the data acquired through these processes requires careful...

23. Discover committor-consistent path by neural networks

Haochuan Chen (University of Lorraine)

9/8/23, 10:00 AM

A significant challenge faced by atomistic simulations is the difficulty, and often impossibility, to sample the transitions between metastable states of the free-energy landscape associated to slow molecular processes. Importance-sampling schemes represent an appealing option to accelerate the underlying dynamics by smoothing out the relevant free-energy barriers, but require the definition...

27. Stochastic Interpolants: A unifying framework for flows and diffusions

Michael Albergo (New York University)

9/8/23, 10:30 AM

I will discuss recent work on unifying flow-based and diffusion based methods through a
generative modeling paradigm we call stochastic interpolants. These models enable the use of a broad class of continuous-time stochastic processes called `stochastic interpolants' to bridge any two arbitrary probability density functions exactly in finite time. These interpolants are built by combining...

30. Beyond translational flows in non-reversible sampling

Tristan Guyon (Laboratoire de Mathématiques Blaise Pascal, Université Clermont-Auvergne)

9/8/23, 11:30 AM

Breaking reversibility in Monte Carlo algorithms often leads to substantial accelerations in sampling complex systems. Event-Chain Monte Carlo (ECMC) has allowed to investigate the bidimensional hard-sphere phase transition, building on non-reversible continuous translational moves. However, more general systems require rotations of some sort to thermalize.
In this work, we build on the...

25. Sampling efficiency of transverse forces in dense liquids

Federico Ghimenti (Université Paris Cité)

9/8/23, 12:00 PM

Sampling the Boltzmann distribution using forces that violate detailed balance can be faster than with the equilibrium evolution, but the acceleration depends on the nature of the nonequilibrium drive and the physical situation. Here, we study the efficiency of forces transverse to energy gradients in dense liquids through a combination of techniques: Brownian dynamics simulations, exact...

26. Parallel Replica algorithm for Langevin dynamics and Adaptative Metadynamics

Mouad Ramil

9/8/23, 12:30 PM

This talk will be divided into two independent parts. The first part shall focus on the extension of the formalization of an algorithm (Parallel Replica) to the case of the Langevin dynamics. Parallel Replica is used in material science to sample rare-events and consists in a parallelization in time of the sampling. It can be formalized using the notion of quasi-stationary distributions which...

13. Anomalous thermal relaxations of physical systems

Marija Vucelja (University of Virginia)

9/13/23, 10:00 AM

Rapid cooling or heating of a physical system can lead to unusual thermal relaxation phenomena. A prime example of anomalous thermal relaxation is the Mpemba effect. The phenomenon occurs when a system prepared at a hot temperature overtakes an identical system prepared at a warm temperature and equilibrates faster to the cold environment. A similar effect exists in heating. Comparing two...

43. Cooperative learning: improving inference through sampling with coupled systems

Giovanni CATANIA (Universidad Complutense Madrid)

9/15/23, 10:00 AM

Constraint Satisfaction problems (CSPs) deal with finding a solution to a set of variables that satisfy a set of constraints. In the last decade, it has been found that many CSPs can have different levels of computational hardness when the number of constraints is changed. The same issue arises in inference problems in the so-called planted setting, where a planted configuration always exists...

44. Anharmonic thermo-elasticity of tungsten from accelerated Bayesian adaptive biasing force calculations with data-driven force fields

Anruo Zhong (Université Paris-Saclay, CEA, Service de Recherche en Corrosion et Comportement des Matériaux, SRMP, 91191 Gif-sur-Yvette, France)

9/15/23, 10:30 AM

The elastic properties of tungsten, a ubiquitous material in future energy systems, are investigated up to its melting temperature by means of a data-driven approach. The proposed workflow combines machine learning of the force field and enhanced sampling of the crystalline structure. While the machine learning force field achieves the accuracy of ab initio calculations, its implementation in...

46. Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective

Davide Ghio (EPFL)

9/18/23, 10:00 AM

Recent years witnessed the development of powerful generative models based on flows, diffusion or autoregressive neural networks, achieving remarkable success in generating data from examples with applications in a broad range of areas. A theoretical analysis of the performance and understanding of the limitations of these methods remain, however, challenging.

In this talk, I present our...

24. Disentangling representations in RBMs without adversaries

Jorge Fernández de Cossío Díaz (PSL)

9/18/23, 10:30 AM

A goal of unsupervised machine learning is to build representations of complex high-dimensional data, with simple relations to their properties. Such disentangled representations make it easier to interpret the significant latent factors of variation in the data, as well as to generate new data with desirable features. The methods for disentangling representations often rely on an adversarial...

14. Renormalization Group Approach for Machine Learning Hamiltonian

Misaki Ozawa (CNRS, Univ. Grenoble Alpes, France)

9/20/23, 10:00 AM

Reconstructing, or generating, high dimensional probability distributions starting from data is a central problem in machine learning and data sciences.
We will present a method —The Wavelet Conditional Renormalization Group —that combines ideas from physics (renormalization group theory) and computer science (wavelets, Monte-Carlo sampling, etc.). The Wavelet Conditional Renormalization...

31. Sampling with Neural Hamiltonian Flows

Vincent Souveton (LMBP - Université Clermont Auvergne)

9/20/23, 10:30 AM

Normalizing Flows (NF) are Generative models which transform a simple prior distribution into the desired target. They however require the design of an invertible mapping whose Jacobian determinant has to be computable. Recently introduced, Neural Hamiltonian Flows (NHF) are Hamiltonian dynamics-based flows, which are continuous, volume-preserving and invertible and thus make for natural...

48. Conditioning and variance reduction schemes for more accurate estimation of thermodynamic and transport properties of metal alloys - Manuel Athènes

9/21/23, 10:00 AM

The Monte Carlo method is a stochastic simulation approach mainly used to estimate multi-dimensional integrals. This is traditionally done by generating a Markov chain of states and then implementing an estimator. In this context, conditioning is a trick consisting in doing part of the job in closed form, through numerical quadrature, so as to reduce the statistical variance associated with...

49. Active particle systems through the lens of piecewise deterministic Markov processes

Léo Hahn (Université Clermont Auvergne)

9/22/23, 10:00 AM

Run-and-tumble particles are a paradigmatic model in out-of-equilibrium physics that exhibits interesting phenomena not found in their passive counterparts such as motility-induced phase separation. I will present the long-time behavior of a pair of such particles with hard-core interactions on a unidimensional torus and on a line by casting them as a piecewise deterministic Markov process. I...

50. Splitting schemes for second order approximations of piecewise-deterministic Markov processes

Andrea Bertazzi (CMAP École polytechnique)

9/22/23, 10:30 AM

Piecewise deterministic Markov processes (PDMPs) received substantial interest in recent years as an alternative to classical Markov chain Monte Carlo algorithms. While theoretical properties of PDMPs have been studied extensively, their practical implementation remains limited to specific applications in which bounds on the gradient of the negative log-target can be derived. In order to...

22. Arbitrarily accurate, nonparametric coarse graining with Markov renewal processes and the Mori-Zwanzig formulation

David Aristoff

Stochastic dynamics, such as molecular dynamics, are important in many scientific applications. Summarizing and analyzing the results of such simulations is often challenging, due to the high dimension in which simulations are carried out (and consequently to the very large amount of data that is typically generated).
Coarse graining is a popular technique for addressing this problem by...