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We benchmark a set of quantum-chemistry methods, including multitrajectory Ehrenfest, fewest-switches surface-hopping, and multiconfigurational-Ehrenfest dynamics, against exact quantum-many-body techniques by studying real-time dynamics in…

Recent advances in learning dynamical systems from data have shown significant promise. However, many existing methods assume access to the full state of the system -- an assumption that is rarely satisfied in practice, where systems are…

Machine Learning · Computer Science 2026-03-10 Thibault Monsel , Onofrio Semeraro , Lionel Mathelin , Guillaume Charpiat

Only a subset of degrees of freedom are typically accessible or measurable in real-world systems. As a consequence, the proper setting for empirical modeling is that of partially-observed systems. Notably, data-driven models consistently…

Statistical Mechanics · Physics 2023-04-18 Adam Rupe , Velimir V. Vesselinov , James P. Crutchfield

Nonequilibrium Green's functions provide a powerful framework for studying quantum many-body dynamics including the laser-induced dynamics in solids. The Non-Equilibrium Systems Simulation package (NESSi) offers an efficient platform for…

We introduce a simple ansatz for the wavefunction of a many-body system based on coupled forward and backward-propagating semiclassical trajectories. This method is primarily aimed at, but not limited to, treating nonequilibrium dynamics in…

Chemical Physics · Physics 2018-05-01 Shunsuke A. Sato , Aaron Kelly , Angel Rubio

Density estimation is a fundamental task in statistics and machine learning applications. Kernel density estimation is a powerful tool for non-parametric density estimation in low dimensions; however, its performance is poor in higher…

Machine Learning · Computer Science 2022-08-08 Joseph A. Gallego , Fabio A. González

Riemannian submanifold optimization with momentum is computationally challenging because, to ensure that the iterates remain on the submanifold, we often need to solve difficult differential equations. Here, we simplify such difficulties…

Machine Learning · Statistics 2024-03-19 Wu Lin , Valentin Duruisseaux , Melvin Leok , Frank Nielsen , Mohammad Emtiyaz Khan , Mark Schmidt

We introduce a machine-learning-based coarse-grained molecular dynamics (CGMD) model that faithfully retains the many-body nature of the inter-molecular dissipative interactions. Unlike common empirical CG models, the present model is…

Computational Physics · Physics 2023-12-01 Liyao Lyu , Huan Lei

Algebraic multigrid (AMG) methods derive their optimal efficiency from the interplay between a relaxation process and a corresponding coarse grid correction. In many standard formulations, relaxation and coarse-graining are analyzed and…

Numerical Analysis · Mathematics 2026-03-30 Rayan Moussa , Karsten Kahl

Reconstruction of equations of motion from incomplete or noisy data and dimension reduction are two fundamental problems in the study of dynamical systems with many degrees of freedom. For the latter extensive efforts have been made but…

Statistical Mechanics · Physics 2011-02-08 Jianhua Xing , Kenneth S Kim

There is current interest in dynamical description of dif- ferent decompositions of a quantum system into subsystems. We investi- gate usefulness of the Nakajima-Zwanzig projection method in this context. Particularly, we are interested in…

Quantum Physics · Physics 2016-11-04 M. Arsenijevic , J. Jeknic-Dugic , M. Dugic

Autoregressive generative PDE solvers can be accurate one step ahead yet drift over long rollouts, especially in coarse-to-fine regimes where each step must regenerate unresolved fine scales. This is the regime of diffusion and…

Machine Learning · Computer Science 2026-02-09 Victor Armegioiu

We present a general numerical approach for constructing governing equations for unknown dynamical systems when only data on a subset of the state variables are available. The unknown equations for these observed variables are thus a…

Machine Learning · Statistics 2020-04-21 Xiaohan Fu , Lo-Bin Chang , Dongbin Xiu

Including quantum mechanical effects on the dynamics of nuclei in the condensed phase is challenging, because the complexity of exact methods grows exponentially with the number of quantum degrees of freedom. Efforts to circumvent these…

Chemical Physics · Physics 2015-06-23 Mariana Rossi , Hanchao Liu , Francesco Paesani , Joel Bowman , Michele Ceriotti

Diffusion is an ubiquitous phenomenon. It is a widespread belief that as long as the area under a current autocorrelation function converges in time, the corresponding spatiotemporal density dynamics should be diffusive. This may be viewed…

Statistical Mechanics · Physics 2025-07-04 Scott D. Linz , Jiaozi Wang , Robin Steinigeweg , Jochen Gemmer

Theoretical descriptions of non equilibrium dynamics of quantum many-body systems essentially employ either (i) explicit treatments, relying on truncation of the expansion of the many-body wave function, (ii) compressed representations of…

We present a new method which combines Car-Parrinello and Born-Oppenheimer molecular dynamics in order to accelerate density functional theory based ab-initio simulations. Depending on the system a gain in efficiency of one to two orders of…

Materials Science · Physics 2007-05-23 Thomas D. Kühne , Matthias Krack , Fawzi R. Mohamed , Michele Parrinello

We present a macroscopic theory of electroencephalogram (EEG) dynamics based on the laws of motion that govern atomic and molecular motion. The theory is an application of Zwanzig-Mori projection operators. The result is a simple equation…

Neurons and Cognition · Quantitative Biology 2009-07-14 David Hsu , Murielle Hsu

In several domains of physics, including first principle simulations and classical models for polarizable systems, the minimization of an energy function with respect to a set of auxiliary variables must be performed to define the dynamics…

Computational Physics · Physics 2021-08-18 David D. Girardier , Alessandro Coretti , Giovanni Ciccotti , Sara Bonella

The numerical simulation of electromagnetic transients in fusion devices is essential for analyzing plasma stability and disruptive events. However, it remains computationally demanding due to the large-scale dense systems arising from…

Numerical Analysis · Mathematics 2026-05-28 Salvatore Ventre