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Joint Energy-based Model (JEM) is a recently proposed hybrid model that retains strong discriminative power of modern CNN classifiers, while generating samples rivaling the quality of GAN-based approaches. In this paper, we propose a…

Machine Learning · Computer Science 2021-09-22 Xiulong Yang , Shihao Ji

Molecular relaxation, finding the equilibrium state of a non-equilibrium structure, is an essential component of computational chemistry to understand reactivity. Classical force field (FF) methods often rely on insufficient local energy…

Being able to effectively locate saddle (and other fixed) points in dynamical systems holds tremendous implications in a number of applications in engineering and science, among which the study of rare events in molecular simulations stands…

Other Condensed Matter · Physics 2025-02-06 Eliodoro Chiavazzo

We introduce a straightforward, single-ensemble, path sampling approach to calculate free energy differences based on Jarzynski's relation. For a two-dimensional ``toy'' test system, the new (minimally optimized) method performs roughly one…

Computational Physics · Physics 2009-11-10 F. Marty Ytreberg , Daniel M. Zuckerman

We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions. REGS is a particle method that seeks a sequence of simple nonlinear transforms iteratively pushing the initial samples from a reference…

Machine Learning · Statistics 2021-10-07 Xingdong Feng , Yuan Gao , Jian Huang , Yuling Jiao , Xu Liu

We describe short-time kinetic and steady-state properties of the non--equilibrium phases, namely, solid, liquid and gas anisotropic phases in a driven Lennard-Jones fluid. This is a computationally-convenient two-dimensional model which…

Soft Condensed Matter · Physics 2009-11-11 J. Marro , P. L. Garrido , M. Diez-Minguito

Equilibrium quantum systems are often described by a gas of weakly-interacting normal modes. Bringing such systems far from equilibrium, however, can drastically enhance mode-to-mode interactions. Understanding the resulting liquid is a…

Quantum Physics · Physics 2026-02-26 Anton V. Bubis , Lucia Vigliotti , Maksym Serbyn , Andrew P. Higginbotham

Processing sets or other unordered, potentially variable-sized inputs in neural networks is usually handled by aggregating a number of input tensors into a single representation. While a number of aggregation methods already exist from…

Machine Learning · Computer Science 2022-07-05 Sergey Bartunov , Fabian B. Fuchs , Timothy Lillicrap

A nonequilibrium statistical operator method is developed for ensembles of particles obeying non-Hamiltonian equations of motion in classical phase space. The main consequences of non-zero compressibility of phase space are examined in…

Statistical Mechanics · Physics 2007-05-23 Alexander V. Zhukov , Jianshu Cao

Cryo-electron microscopy (cryo-EM) has recently become a premier method for obtaining high-resolution structures of biological macromolecules. However, it is limited to biomolecular samples with low conformational heterogeneity, where all…

Predictive simulations of complex systems are essential for applications ranging from weather forecasting to drug design. The veracity of these predictions hinges on their capacity to capture the effective system dynamics. Massively…

Computational Physics · Physics 2021-10-20 Pantelis R. Vlachas , Georgios Arampatzis , Caroline Uhler , Petros Koumoutsakos

Non-equilibrium Monte Carlo simulations based on Jarzynski's equality are a well-understood method to compute differences in free energy and also to sample from a target probability distribution without the need to thermalize the system…

High Energy Physics - Lattice · Physics 2024-10-07 Andrea Bulgarelli , Elia Cellini , Alessandro Nada

Nested sampling (NS) has emerged as a powerful tool for exploring thermodynamic properties in materials science. However, its efficiency is often hindered by the limitations of Markov chain Monte Carlo (MCMC) sampling. In strongly…

Statistical Mechanics · Physics 2025-07-31 Nico Unglert , Livia Bartók Pártay , Georg K. H. Madsen

The Jarzynski estimator is a powerful tool that uses nonequilibrium statistical physics to numerically obtain partition functions of probability distributions. The estimator reconstructs partition functions with trajectories of the…

Statistical Mechanics · Physics 2022-05-17 Nobumasa Ishida , Yoshihiko Hasegawa

The weighted ensemble (WE) method, an enhanced sampling approach based on periodically replicating and pruning trajectories in a set of parallel simulations, has grown increasingly popular for computational biochemistry problems, due in…

Computational Physics · Physics 2023-06-23 D. Aristoff , J. Copperman , G. Simpson , R. J. Webber , D. M. Zuckerman

The theoretical analysis of many problems in physics, astronomy and applied mathematics requires an efficient numerical exploration of multimodal parameter spaces that exhibit broken ergodicity. Monte Carlo methods are widely used to deal…

Statistical Mechanics · Physics 2014-09-02 Stefano Martiniani , Jacob D. Stevenson , David J. Wales , Daan Frenkel

In the global framework of finding an axiomatic derivation of nonequilibrium Statistical Mechanics from fundamental principles, such as the maximum path entropy -- also known as Maximum Caliber principle -- , this work proposes an…

Statistical Mechanics · Physics 2017-06-28 Diego González , Sergio Davis

A method for adaptive model order reduction for nonsmooth discrete element simulation is developed and analysed in numerical experiments. Regions of the granular media that collectively move as rigid bodies are substituted with rigid bodies…

Computational Physics · Physics 2015-12-02 Martin Servin , Da Wang

The convergence to equilibrium for renormalised solutions to nonlinear reaction-diffusion systems is studied. The considered reaction-diffusion systems arise from chemical reaction networks with mass action kinetics and satisfy the complex…

Analysis of PDEs · Mathematics 2018-05-09 Klemens Fellner , Bao Q. Tang

State estimation is key to both analyzing physical mechanisms and enabling real-time control of fluid flows. A common estimation approach is to relate sensor measurements to a reduced state governed by a reduced-order model (ROM). (When…

Fluid Dynamics · Physics 2020-06-10 Nirmal J. Nair , Andres Goza