Cover Image

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Heimbach, Lothar, Kaltenbach, Sebastian, Karnakov, Petr, Alexander, Francis J., Koumoutsakos, Petros
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning
by: von Bassewitz, Jan-Philipp, et al.
Published: (2024)

Generative Learning of the Solution of Parametric Partial Differential Equations Using Guided Diffusion Models and Virtual Observations
by: Gao, Han, et al.
Published: (2024)

Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning
by: Fischer, Paul, et al.
Published: (2025)

Generative Learning for Forecasting the Dynamics of Complex Systems
by: Gao, Han, et al.
Published: (2024)

Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows
by: Gao, Han, et al.
Published: (2025)

Contactless Precision Steering of Particles in a Fluid inside a Cube with Rotating Walls
by: Amoudruz, Lucas, et al.
Published: (2025)

Optimizing a DIscrete Loss (ODIL) to solve forward and inverse problems for partial differential equations using machine learning tools
by: Karnakov, Petr, et al.
Published: (2022)

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss
by: Karnakov, Petr, et al.
Published: (2025)

Data-Driven Discovery of Interpretable Kalman Filter Variants through Large Language Models and Genetic Programming
by: Saketos, Vasileios, et al.
Published: (2025)

Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss
by: Amoudruz, Lucas, et al.
Published: (2025)

Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems
by: Heidenreich, Hunter S., et al.
Published: (2024)

Controlling Topological Defects in Polar Fluids via Reinforcement Learning
by: Singh, Abhinav, et al.
Published: (2025)

Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning
by: Amoudruz, Lucas, et al.
Published: (2024)

PICL: Physics Informed Contrastive Learning for Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2024)

Generative Learning for Forecasting the Dynamics of Complex Systems
by: Gao, Hang, et al.
Published: (2025)

Physics Informed Token Transformer for Solving Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2023)

What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning
by: Mohan, Arvind, et al.
Published: (2024)

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks
by: Xu, Hao, et al.
Published: (2025)

Learning Domain-Independent Green's Function For Elliptic Partial Differential Equations
by: Negi, Pawan, et al.
Published: (2024)

Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks
by: Zhai, Jucai, et al.
Published: (2026)

Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
by: Balcerak, Michal, et al.
Published: (2025)

BEACONS: Bounded-Error, Algebraically-Composable Neural Solvers for Partial Differential Equations
by: Gorard, Jonathan, et al.
Published: (2026)

Discovering Interpretable Ordinary Differential Equations from Noisy Data
by: Golder, Rahul, et al.
Published: (2025)

Projected Neural Differential Equations for Learning Constrained Dynamics
by: White, Alistair, et al.
Published: (2024)

Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
by: White, Alistair, et al.
Published: (2023)

Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equations
by: Iyer, Narayan S, et al.
Published: (2025)

Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
by: Zheng, Haoyang, et al.
Published: (2025)

A Bayesian Approach for Discovering Time- Delayed Differential Equation from Data
by: Chowdhury, Debangshu, et al.
Published: (2025)

Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks
by: Koumpanakis, Michail, et al.
Published: (2024)

Data-driven shape inference in three-dimensional steady state supersonic flows using ODIL and JAX-Fluids
by: Buhendwa, Aaron B., et al.
Published: (2024)

Solving Partial Differential Equations in Different Domains by Operator Learning method Based on Boundary Integral Equations
by: Meng, Bin, et al.
Published: (2024)

Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations
by: Nagy-Huber, Monika, et al.
Published: (2023)

Self-Adaptive Physics-Informed Quantum Machine Learning for Solving Differential Equations
by: Setty, Abhishek, et al.
Published: (2023)

Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics Simulations
by: Xue, Tingkai, et al.
Published: (2025)

A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations
by: Hu, Hang, et al.
Published: (2024)

Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
by: Kim, Chanyoung, et al.
Published: (2026)

Solving Partial Differential Equations with Equivariant Extreme Learning Machines
by: Harder, Hans, et al.
Published: (2024)

Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization
by: Balcerak, Michal, et al.
Published: (2024)

Learning Nonlinear Dynamics in Physical Modelling Synthesis using Neural Ordinary Differential Equations
by: Zheleznov, Victor, et al.
Published: (2025)

Self-supervised Pretraining for Partial Differential Equations
by: Madhavan, Varun, et al.
Published: (2024)