Imagen de Portada

Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Man, Hai, Wang, Chaobo, Li, Jia-Rui, Tian, Yuping, Chen, Shu-Gang
Formato:	Preprint
Publicado:	2026
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

CoRMF: Criticality-Ordered Recurrent Mean Field Ising Solver
por: Pan, Zhenyu, et al.
Publicado: (2024)

Selecting Optimal Variable Order in Autoregressive Ising Models
por: Biswal, Shiba, et al.
Publicado: (2026)

IsingFormer: Augmenting Parallel Tempering With Learned Proposals
por: Bunaiyan, Saleh, et al.
Publicado: (2025)

Learning Beyond Optimization: Stress-Gated Dynamical Regime Regulation in Autonomous Systems
por: Ran, Sheng
Publicado: (2026)

Identifying Ising and percolation phase transitions based on KAN method
por: Xu, Dian, et al.
Publicado: (2025)

Efficient Identification of Critical Transitions via Flow Matching: A Scalable Generative Approach for Many-Body Systems
por: Lee, Qian-Rui, et al.
Publicado: (2025)

Computationally sufficient statistics for Ising models
por: Jayakumar, Abhijith, et al.
Publicado: (2026)

Effectiveness of Binary Autoencoders for QUBO-Based Optimization Problems
por: Abe, Tetsuro, et al.
Publicado: (2026)

Unilateral Criticality and Phase Transition in the Cavity-Ising Model
por: Rao, Zeyu, et al.
Publicado: (2025)

Generative Learning of Continuous Data by Tensor Networks
por: Meiburg, Alex, et al.
Publicado: (2023)

Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large Deviations
por: Gillman, Edward, et al.
Publicado: (2022)

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)

Exact Combinatorial Density of States for the Critical 1D Ising Model
por: Castorene, Bastian, et al.
Publicado: (2025)

Discovering quantum phenomena with Interpretable Machine Learning
por: de Schoulepnikoff, Paulin, et al.
Publicado: (2026)

Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics
por: Casagrande, Heitor P., et al.
Publicado: (2024)

Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors
por: Kim, Gilhan, et al.
Publicado: (2026)

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

Statistics of Min-max Normalized Eigenvalues in Random Matrices
por: Nakada, Hyakka, et al.
Publicado: (2025)

Factorization Machine with Quadratic-Optimization Annealing for RNA Inverse Folding and Evaluation of Binary-Integer Encoding and Nucleotide Assignment
por: Kikuchi, Shuta, et al.
Publicado: (2026)

Learning quantum many-body data locally: A provably scalable framework
por: Chinzei, Koki, et al.
Publicado: (2025)

Local Diffusion Models and Phases of Data Distributions
por: Hu, Fangjun, et al.
Publicado: (2025)

Topological Quantum Criticality in Quasiperiodic Ising Chain
por: Yang, Sheng, et al.
Publicado: (2026)

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

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

Intrinsic Dimensionality of Fermi-Pasta-Ulam-Tsingou High-Dimensional Trajectories Through Manifold Learning: A Linear Approach
por: Marchetti, Gionni
Publicado: (2024)

Coarse-Grained Boltzmann Generators
por: Chen, Weilong, et al.
Publicado: (2026)

Latent Thermodynamic Flows: Unified Representation Learning and Generative Modeling of Temperature-Dependent Behaviors from Limited Data
por: Qiu, Yunrui, et al.
Publicado: (2025)

Reinforcement Learning for Quantum Technology
por: Bukov, Marin, et al.
Publicado: (2026)

Three-dimensional Ising models -- Critical Parameters using $ε$-convergence method
por: Vismaya, M V, et al.
Publicado: (2024)

Neural-network quantum state study of the long-range antiferromagnetic Ising chain
por: Kim, Jicheol, et al.
Publicado: (2023)

Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond
por: Cai, Xiansheng, et al.
Publicado: (2025)

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

The Eggbox Ising Model
por: Shen, Mutian, et al.
Publicado: (2025)

Reinforcement Learning for Microcanonical Graph Ensemble with Assortativity Constraints
por: Choi, Hoyun, et al.
Publicado: (2026)

Improving FMQA via Initial Training Data Design Considering Marginal Bit Coverage in One-Hot Encoding
por: Hayashi, Taiga, et al.
Publicado: (2026)

Critical temperatures of two- and three-dimensional Ferromagnetic Ising models- A hierarchy
por: Vismaya, M V, et al.
Publicado: (2024)

Learning the Intrinsic Dimensionality of Fermi-Pasta-Ulam-Tsingou Trajectories: A Nonlinear Approach using a Deep Autoencoder Model
por: Marchetti, Gionni
Publicado: (2026)

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

Quantum Inception Score
por: Sone, Akira, et al.
Publicado: (2023)

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