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Entropy production (EP) is a central measure in nonequilibrium thermodynamics, as it can quantify the irreversibility of a process as well as its energy dissipation in special cases. Using the time-reversal asymmetry in a system's path…

Statistical Mechanics · Physics 2022-04-26 Dong-Kyum Kim , Sangyun Lee , Hawoong Jeong

Stochastic chains represent a wide and key variety of phenomena in many branches of science within the context of Information Theory and Thermodynamics. They are typically approached by a sequence of independent events or by a memoryless…

Statistical Mechanics · Physics 2017-03-06 J. Ricardo Arias-Gonzalez

We study the convergence of stochastic time-discretization schemes for evolution equations driven by random velocity fields, including examples like stochastic gradient descent and interacting particle systems. Using a unified framework…

Functional Analysis · Mathematics 2025-05-28 Giulia Cavagnari , Giuseppe Savaré , Giacomo Enrico Sodini

We critically examine nuclear matter and neutron matter equation of state (EoS) parameters by using best available terrestrial experimental results. The nuclear incompression modulus $K_{\infty}$ is re-examined in comparisons with RPA…

Nuclear Theory · Physics 2019-09-04 H. Sagawa , S. Yoshida , Li-Gang Cao

A phase space distribution associated with a quantum state was previously proposed, which incorporates a specific epistemic restriction parameterized by a global random variable on the order of Planck constant, transparently manifesting…

Quantum Physics · Physics 2020-05-15 Agung Budiyono

In dealing with high-dimensional data, factor models are often used for reducing dimensions and extracting relevant information. The spectrum of covariance matrices from power data exhibits two aspects: 1) bulk, which arises from random…

Applications · Statistics 2019-10-22 Xin Shi , Robert Qiu

Learning disentangled representations is a key step towards effectively discovering and modelling the underlying structure of environments. In the natural sciences, physics has found great success by describing the universe in terms of…

Machine Learning · Computer Science 2020-10-27 Robin Quessard , Thomas D. Barrett , William R. Clements

Deep learning (DL) models, despite their remarkable success, remain vulnerable to small input perturbations that can cause erroneous outputs, motivating the recent proposal of probabilistic robustness (PR) as a complementary alternative to…

Computer Vision and Pattern Recognition · Computer Science 2025-11-24 Zheng Wang , Yi Zhang , Siddartha Khastgir , Carsten Maple , Xingyu Zhao

Pattern formation is a widely observed phenomenon in diverse fields including materials physics, developmental biology and ecology, among many others. The physics underlying the patterns is specific to the mechanisms, and is encoded by…

Computational Engineering, Finance, and Science · Computer Science 2024-03-28 Z. Wang , X. Huan , K. Garikipati

Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material's thermodynamic state variables -- specifically pressure, volume/density, energy, and temperature. EOS models are…

Data Analysis, Statistics and Probability · Physics 2024-02-26 Himanshu Sharma , Jim A. Gaffney , Dimitrios Tsapetis , Michael D. Shields

Many systems in physics, engineering, and biology exhibit multiscale stochastic dynamics, where low-dimensional slow variables evolve under the influence of high-dimensional fast processes. In practice, observations are often limited to a…

Machine Learning · Statistics 2026-05-12 Anan Saha , Arnab Ganguly

A solid state electronic nanodevice is an intrinsically open quantum system, exchanging both energy with the host material and carriers with connected reservoirs. Its out-of-equilibrium behavior is determined by a non-trivial interplay…

Mesoscale and Nanoscale Physics · Physics 2013-09-18 Fabrizio Dolcini , Rita Claudia Iotti , Fausto Rossi

Several physical systems in condensed matter have been modeled approximating their constituent particles as hard objects. The hard spheres model has been indeed one of the cornerstones of the computational and theoretical description in…

Computational Physics · Physics 2015-05-13 Cristiano De Michele

This paper considers the problem of data-driven prediction of partially observed systems using a recurrent neural network. While neural network based dynamic predictors perform well with full-state training data, prediction with partial…

Systems and Control · Electrical Eng. & Systems 2023-04-07 Debdipta Goswami

The concept of entropy in nonequilibrium macroscopic systems is investigated in the light of an extended equation of motion for the density matrix obtained in a previous study. It is found that a time-dependent information entropy can be…

Statistical Mechanics · Physics 2009-11-10 W. T. Grandy

Stochastic flexural vibrations of small-scale Bernoulli-Euler beams with external damping are investigated by stress-driven nonlocal mechanics. Damping effects are simulated considering viscous interactions between beam and surrounding…

Dynamical systems theory has long provided a foundation for understanding evolving phenomena across scientific domains. Yet, the application of this theory to complex real-world systems remains challenging due to issues in mathematical…

Machine Learning · Computer Science 2024-11-05 Samuel A. Moore , Brian P. Mann , Boyuan Chen

The stochastic entropy generated during the evolution of a system interacting with an environment may be separated into three components, but only two of these have a non-negative mean. The third component of entropy production is…

Statistical Mechanics · Physics 2013-05-30 Ian J. Ford , Richard E. Spinney

We report three-dimensional particle mechanics static calculations that predict the microstructure evolution during die-compaction of elastic spherical particles up to relative densities close to one. We employ a nonlocal contact…

Soft Condensed Matter · Physics 2016-08-03 Marcial Gonzalez , Alberto M. Cuitino

Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals provides detailed insights into intermolecular bonding by decomposing the total molecular binding energy into physically meaningful components. Here, we…

Chemical Physics · Physics 2025-09-25 Hossein Tahmasbi , Michael Beerbaum , Bartosz Brzoza , Attila Cangi , Thomas D. Kühne