:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wang, Shihao, Qian, Qipeng, Wang, Jingquan
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Mathematical Physics
Online Access:	https://arxiv.org/abs/2602.00872
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Symbolic Neural Ordinary Differential Equations
by: Li, Xin, et al.
Published: (2025)

Symbolic Recovery of Differential Equations: The Identifiability Problem
by: Scholl, Philipp, et al.
Published: (2022)

High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces
by: Shao, Shihao, et al.
Published: (2024)

A Learning-based Domain Decomposition Method
by: Wu, Rui, et al.
Published: (2025)

General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
by: Wang, Sen, et al.
Published: (2024)

Learning Nonholonomic Dynamics with Constraint Discovery
by: Wang, Baiyue, et al.
Published: (2024)

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
by: Mirzabeigi, Elmira, et al.
Published: (2025)

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

Scientific Machine Learning of Chaotic Systems Discovers Governing Equations for Neural Populations
by: Chesebro, Anthony G., et al.
Published: (2025)

ASPINN: An asymptotic strategy for solving singularly perturbed differential equations
by: Wang, Sen, et al.
Published: (2024)

LLM4ED: Large Language Models for Automatic Equation Discovery
by: Du, Mengge, et al.
Published: (2024)

Neumann Series-based Neural Operator for Solving Inverse Medium Problem
by: Liu, Ziyang, et al.
Published: (2024)

A phase transition in sampling from Restricted Boltzmann Machines
by: Kwon, Youngwoo, et al.
Published: (2024)

Continual Learning as a Multiphase Moving-Boundary Problem
by: Khilar, Snigdha Chandan
Published: (2026)

Loss Jump During Loss Switch in Solving PDEs with Neural Networks
by: Wang, Zhiwei, et al.
Published: (2024)

Physics-Informed Deep Learning of Rate-and-State Fault Friction
by: Rucker, Cody, et al.
Published: (2023)

Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation law
by: Zhang, Jingdong, et al.
Published: (2024)

RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles
by: Behnoudfar, Pouria, et al.
Published: (2025)

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling
by: Zang, Yaohua, et al.
Published: (2025)

The role of interface boundary conditions and sampling strategies for Schwarz-based coupling of projection-based reduced order models
by: Wentland, Christopher R., et al.
Published: (2024)

Uncertainty Quantification for Machine Learning-Based Prediction: A Polynomial Chaos Expansion Approach for Joint Model and Input Uncertainty Propagation
by: Du, Xiaoping
Published: (2025)

Deep Learning based discovery of Integrable Systems
by: Lal, Shailesh, et al.
Published: (2025)

Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition
by: Bian, Shirui, et al.
Published: (2024)

Statistical Complexity of Quantum Learning
by: Banchi, Leonardo, et al.
Published: (2023)

Weak Generative Sampler to Efficiently Sample Invariant Distribution of Stochastic Differential Equation
by: Cai, Zhiqiang, et al.
Published: (2024)

Domain Decomposition-based coupling of Operator Inference reduced order models via the Schwarz alternating method
by: Moore, Ian, et al.
Published: (2024)

tn4ml: Tensor Network Training and Customization for Machine Learning
by: Puljak, Ema, et al.
Published: (2025)

FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
by: Menon, Sidharth S., et al.
Published: (2026)

Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments
by: Mishra, Aashwin, et al.
Published: (2026)

Generative Path-Finding Method for Wasserstein Gradient Flow
by: Liu, Chengyu, et al.
Published: (2026)

Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data
by: Geng, Ru, et al.
Published: (2026)

Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing
by: Basterrech, Sebastian, et al.
Published: (2025)

Efficient Error Certification for Physics-Informed Neural Networks
by: Eiras, Francisco, et al.
Published: (2023)

Physics-informed neural networks to solve inverse problems in unbounded domains
by: Pérez-Bernal, Gregorio, et al.
Published: (2025)

BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows $-$ a case study in optimal monitor well placement for CO$_2$ sequestration
by: Orozco, Rafael, et al.
Published: (2024)

A review of graph neural network applications in mechanics-related domains
by: Zhao, Yingxue, et al.
Published: (2024)

Symbolic identification of tensor equations in multidimensional physical fields
by: Chen, Tianyi, et al.
Published: (2025)

PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure
by: Wu, Junyi, et al.
Published: (2025)

A DNN Biophysics Model with Topological and Electrostatic Features
by: Sliheet, Elyssa, et al.
Published: (2024)