Cover Image

Controlling dynamics of stochastic systems with deep reinforcement learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Mukhamadiarov, Ruslan
Format:	Preprint
Published:	2025
Subjects:	Statistical Mechanics Machine Learning Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Model selection for stochastic dynamics: a parsimonious and principled approach
by: Gerardos, Andonis
Published: (2025)

Hierarchical geometric deep learning enables scalable analysis of molecular dynamics
by: Pengmei, Zihan, et al.
Published: (2025)

Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks
by: Lanzoni, Daniele, et al.
Published: (2023)

Differentiable free energy surface: a variational approach to directly observing rare events using generative deep-learning models
by: Li, Shuo-Hui, et al.
Published: (2026)

Agent-based Monte Carlo simulations for reaction-diffusion models, population dynamics, and epidemic spreading
by: Swailem, Mohamed, et al.
Published: (2025)

Quasi-potential and drift decomposition in stochastic systems by sparse identification
by: Grigorio, Leonardo, et al.
Published: (2024)

Quantum-data-driven dynamical transition in quantum learning
by: Zhang, Bingzhi, et al.
Published: (2024)

Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks
by: Lanzoni, Daniele, et al.
Published: (2025)

Gradient estimators for parameter inference in discrete stochastic kinetic models
by: Burger, Ludwig, et al.
Published: (2026)

Building causation links in stochastic nonlinear systems from data
by: Chibbaro, Sergio, et al.
Published: (2025)

Maximum diffusion reinforcement learning
by: Berrueta, Thomas A., et al.
Published: (2023)

Effects of structural properties of neural networks on machine learning performance
by: Arya, Yash, et al.
Published: (2025)

Riemannian Stochastic Interpolants for Amorphous Particle Systems
by: Grenioux, Louis, et al.
Published: (2025)

Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations
by: Christiansen, Henrik, et al.
Published: (2025)

Estimating Solvation Free Energies with Boltzmann Generators
by: Schebek, Maximilian, et al.
Published: (2025)

Boltzmann Generators for Condensed Matter via Riemannian Flow Matching
by: Hoffmann, Emil, et al.
Published: (2026)

Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning
by: Man, Hai, et al.
Published: (2026)

AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning
by: Kanaujia, Vikas, et al.
Published: (2024)

Computing critical exponents in 3D Ising model via pattern recognition/deep learning approach
by: Burt, Timothy A.
Published: (2024)

Noise-aware neural network for stochastic dynamics simulation
by: Wu, Pei-Fang, et al.
Published: (2024)

Geometric origin of adversarial vulnerability in deep learning
by: Ren, Yixiong, et al.
Published: (2025)

Fundamentals of quantum Boltzmann machine learning with visible and hidden units
by: Wilde, Mark M.
Published: (2025)

Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
by: de Schoulepnikoff, Paulin, et al.
Published: (2025)

Meta-learning of Gibbs states for many-body Hamiltonians with applications to Quantum Boltzmann Machines
by: Bhat, Ruchira V, et al.
Published: (2025)

Dynamical symmetries in the fluctuation-driven regime: an application of Noether's theorem to noisy dynamical systems
by: Vastola, John J.
Published: (2025)

Learning conformational ensembles of proteins based on backbone geometry
by: Wolf, Nicolas, et al.
Published: (2025)

Implicit Binarization via Complex Phase Dynamics in Combinatorial Optimization
by: Cohen, Khen, et al.
Published: (2026)

Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
by: Majumdar, Sauradeep, et al.
Published: (2026)

Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
by: Egenlauf, Patrick, et al.
Published: (2025)

Quantum statistics from classical simulations via generative Gibbs sampling
by: Wang, Weizhou, et al.
Published: (2026)

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
by: Schaaf, Lars L., et al.
Published: (2024)

Time crystalline solitons and their stochastic dynamics in a driven-dissipative ϕ^4 model
by: Luo, Xingdong, et al.
Published: (2025)

Model-free learning of probability flows: Elucidating the nonequilibrium dynamics of flocking
by: Boffi, Nicholas M., et al.
Published: (2024)

Principled model selection for stochastic dynamics
by: Gerardos, Andonis, et al.
Published: (2025)

Learning to erase quantum states: thermodynamic implications of quantum learning theory
by: Zhao, Haimeng, et al.
Published: (2025)

An exact multiple-time-step variational formulation for the committor and the transition rate
by: Lorpaiboon, Chatipat, et al.
Published: (2025)

Unsupervised learning of anomalous diffusion data
by: Muñoz-Gil, Gorka, et al.
Published: (2021)

The committee machine: Computational to statistical gaps in learning a two-layers neural network
by: Aubin, Benjamin, et al.
Published: (2018)

Physics and causally constrained discrete-time neural models of turbulent dynamical systems
by: Falasca, Fabrizio, et al.
Published: (2026)

From latent dynamics to meaningful representations
by: Wang, Dedi, et al.
Published: (2022)