Buchumschlag

Solving Partial Differential Equations with Equivariant Extreme Learning Machines

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Harder, Hans, Rabault, Jean, Vinuesa, Ricardo, Mortensen, Mikael, Peitz, Sebastian
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Machine Learning
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions
von: Holme, Kristian, et al.
Veröffentlicht: (2026)

Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-Bénard convection
von: Vasanth, Joel, et al.
Veröffentlicht: (2024)

Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems
von: Harder, Hans, et al.
Veröffentlicht: (2025)

Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoders
von: Fromme, Fynn, et al.
Veröffentlicht: (2025)

Equivariance and partial observations in Koopman operator theory for partial differential equations
von: Peitz, Sebastian, et al.
Veröffentlicht: (2023)

Learning to Solve Optimization Problems Constrained with Partial Differential Equations
von: Guven, Yusuf, et al.
Veröffentlicht: (2025)

Variational (Energy-Based) Spectral Learning: A Machine Learning Framework for Solving Partial Differential Equations
von: Hammad, M. M.
Veröffentlicht: (2026)

Solving Partial Differential Equations with Random Feature Models
von: Liao, Chunyang
Veröffentlicht: (2024)

Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
von: Jeon, Joongoo, et al.
Veröffentlicht: (2024)

WLNO: Wavelet-Laplace Neural Operator for Solving Partial Differential Equations
von: Abid, Muhammad, et al.
Veröffentlicht: (2026)

Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations
von: Kim, Heechang, et al.
Veröffentlicht: (2026)

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

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

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

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

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
von: Peitz, Sebastian, et al.
Veröffentlicht: (2024)

Finite Expression Method for Solving High-Dimensional Partial Differential Equations
von: Liang, Senwei, et al.
Veröffentlicht: (2022)

Chebyshev Spectral Neural Networks for Solving Partial Differential Equations
von: Yin, Pengsong, et al.
Veröffentlicht: (2024)

Deep Learning-Driven Downscaling for Climate Risk Assessment of Projected Temperature Extremes in the Nordic Region
von: Loganathan, Parthiban, et al.
Veröffentlicht: (2025)

Solving Time-Fractional Partial Integro-Differential Equations Using Tensor Neural Network
von: Lin, Zhongshuo, et al.
Veröffentlicht: (2025)

A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations
von: Yang, Qihong, et al.
Veröffentlicht: (2026)

LordNet: An Efficient Neural Network for Learning to Solve Parametric Partial Differential Equations without Simulated Data
von: Huang, Xinquan, et al.
Veröffentlicht: (2022)

Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection
von: Plotzki, Tim, et al.
Veröffentlicht: (2026)

DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training
von: Yang, Yahong
Veröffentlicht: (2024)

Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method
von: Hardwick, Gareth, et al.
Veröffentlicht: (2024)

Weak-Form Evolutionary Kolmogorov-Arnold Networks for Solving Partial Differential Equations
von: Kim, Bongseok, et al.
Veröffentlicht: (2026)

SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion
von: Hotegni, Sedjro Salomon, et al.
Veröffentlicht: (2025)

Enhancing Adversarial Robustness through Multi-Objective Representation Learning
von: Hotegni, Sedjro Salomon, et al.
Veröffentlicht: (2024)

Learning To Solve Differential Equation Constrained Optimization Problems
von: Di Vito, Vincenzo, et al.
Veröffentlicht: (2024)

FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations
von: Ye, Zaijun, et al.
Veröffentlicht: (2024)

Disentangled Representation Learning for Parametric Partial Differential Equations
von: Liu, Ning, et al.
Veröffentlicht: (2024)

Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning
von: Berndt, Torben, et al.
Veröffentlicht: (2025)

Solving Differential Equations with Constrained Learning
von: Moro, Viggo, et al.
Veröffentlicht: (2024)

Partial Differential Equations in the Age of Machine Learning: A Critical Synthesis of Classical, Machine Learning, and Hybrid Methods
von: Nooraiepour, Mohammad, et al.
Veröffentlicht: (2026)

Scale-Consistent Learning for Partial Differential Equations
von: Li, Zongyi, et al.
Veröffentlicht: (2025)

MAD-NG: Meta-Auto-Decoder Neural Galerkin Method for Solving Parametric Partial Differential Equations
von: Li, Qiuqi, et al.
Veröffentlicht: (2025)

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data
von: Heimbach, Lothar, et al.
Veröffentlicht: (2025)

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

Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
von: Ma, Qinglong, et al.
Veröffentlicht: (2024)

AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations
von: Park, Keon Vin
Veröffentlicht: (2025)