aiqbenchInformații despre AI & calcul cuantic

...

Română · beta. Traducerea a fost făcută automat și este încă în curs de revizuire. Citește pagina în engleză

perspectiva aiqbenchMachine learning

This research applies DeepSets-based neural networks to infer the thermodynamic arrow of time directly from microscopic dynamics in a hard-disk system. By training models solely on temporal ordering constraints, the study bridges statistical mechanics and machine learning to evaluate how physical relaxation processes naturally align with the Boltzmann H-functional structure. The work advances fundamental understanding at the intersection of quantum/statistical physics principles and AI model architecture.

Sursă care acoperăarXiv

Ce este aceastaLectura unui model asupra articolului și a acoperirii conexe, scrisă pentru aiqbench. Nu sunt cuvintele publicației.

Machine Learning the H-theorem

arXiv29 iulie 20262 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2508.14003v4 Announce Type: replace-cross Abstract: The H-theorem provides a microscopic foundation for the Second Law of Thermodynamics and therefore occupies a central place in statistical physics. At the same time, its relation to microscopic reversibility has remained conceptually subtle. To investigate how an arrow of time may be inferred directly from microscopic data, we study the relaxation of randomly initialized hard disks in a periodic box. We construct a permutation-invariant neural network based on the DeepSets architecture. The model is trained only to assign later states a larger scalar value than earlier states. We compare the learned scalar with the Boltzmann H-functional and assess to what extent the dynamics alone lead the model toward the structure implied by the H-theorem.

Abstract:The H-theorem provides a microscopic foundation for the Second Law of Thermodynamics and therefore occupies a central place in statistical physics. At the same time, its relation to microscopic reversibility has remained conceptually subtle. To investigate how an arrow of time may be inferred directly from microscopic data, we study the relaxation of randomly initialized hard disks in a periodic box. We construct a permutation-invariant neural network based on the DeepSets architecture. The model is trained only to assign later states a larger scalar value than earlier states. We compare the learned scalar with the Boltzmann H-functional and assess to what extent the dynamics alone lead the model toward the structure implied by the H-theorem.

Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG)

arXiv:2508.14003 [cond-mat.stat-mech]

(or arXiv:2508.14003v4 [cond-mat.stat-mech] for this version)

Text integral sub licența deschisă a publicației · 129 cuvinte

Citește pe arXiv ↗Întreabă despre acest articol