Imagen de Portada

Controlling dynamics of stochastic systems with deep reinforcement learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Mukhamadiarov, Ruslan
Formato:	Preprint
Publicado:	2025
Materias:	Statistical Mechanics Machine Learning Computational Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

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

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

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

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

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

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

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

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

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

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

Maximum diffusion reinforcement learning
por: Berrueta, Thomas A., et al.
Publicado: (2023)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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