Score: 0

Irreversible Kinetics Emerges from Bayesian Inference over Admissible Histories

Published: January 14, 2026 | arXiv ID: 2601.10763v1

By: Manas V. Upadhyay

Potential Business Impact:

Predicts future events by learning from past patterns.

Business Areas:
A/B Testing Data and Analytics

A probabilistic formulation of irreversible kinetics is introduced in which incrementally admissible histories are weighted by a Gibbs-type measure built from an energy-dissipation action and observation constraints, with Theta controlling epistemic uncertainty. This measure can be interpreted as a Bayesian posterior over histories. In the zero-uncertainty limit, it concentrates on maximum-a-posteriori (MAP) histories, recovering classical deterministic evolution by incremental minimization in the convex generalized-standard-material setting, while allowing multiple competing MAP histories for non-convex energies or temporally coupled constraints. This emergence is demonstrated across seven distinct forward-in-time examples and an inverse inference problem of unknown histories from sparse observations via a global constrained minimum-action principle.

Country of Origin
🇫🇷 France

Page Count
6 pages

Category
Condensed Matter:
Statistical Mechanics