Image de couverture de livre

Gauge-Equivariant Intrinsic Neural Operators for Geometry-Consistent Learning of Elliptic PDE Maps

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Cheng, Pengcheng
Format:	Preprint
Publié:	2026
Sujets:	Artificial Intelligence Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Operator Learning with Domain Decomposition for Geometry Generalization in PDE Solving
par: Huang, Jianing, et autres
Publié: (2025)

Quotient Geometry, Effective Curvature, and Implicit Bias in Simple Shallow Neural Networks
par: Dong, Hang-Cheng, et autres
Publié: (2026)

CATO: Charted Attention for Neural PDE Operators
par: Cheng, Chun-Wun, et autres
Publié: (2026)

Interpretable Operator Learning for Inverse Problems via Adaptive Spectral Filtering: Convergence and Discretization Invariance
par: Dong, Hang-Cheng, et autres
Publié: (2026)

Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
par: Song, Haoze, et autres
Publié: (2026)

Space-Time Continuous PDE Forecasting using Equivariant Neural Fields
par: Knigge, David M., et autres
Publié: (2024)

SFO: Learning PDE Operators via Spectral Filtering
par: Koren, Noam, et autres
Publié: (2026)

Graph Neural PDE Solvers with Conservation and Similarity-Equivariance
par: Horie, Masanobu, et autres
Publié: (2024)

Can We Formally Verify Neural PDE Surrogates? SMT Compilation of Small Fourier Neural Operators
par: Baheri, Ali, et autres
Publié: (2026)

An Operator-Consistent Graph Neural Network for Learning Diffusion Dynamics on Irregular Meshes
par: Li, Yuelian, et autres
Publié: (2025)

From Uniform to Adaptive: General Skip-Block Mechanisms for Efficient PDE Neural Operators
par: Liu, Lei, et autres
Publié: (2025)

Graph Neural Networks for Learning Equivariant Representations of Neural Networks
par: Kofinas, Miltiadis, et autres
Publié: (2024)

CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
par: Hou, Xianglong, et autres
Publié: (2025)

Metric Learning for Clifford Group Equivariant Neural Networks
par: Ali, Riccardo, et autres
Publié: (2024)

SCNO: Spiking Compositional Neural Operator -- Towards a Neuromorphic Foundation Model for Nuclear PDE Solving
par: Roy, Samrendra, et autres
Publié: (2026)

Grounding Continuous Representations in Geometry: Equivariant Neural Fields
par: Wessels, David R, et autres
Publié: (2024)

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks
par: Stupariu, Teodor-Mihai, et autres
Publié: (2026)

Large Language-Geometry Model: When LLM meets Equivariance
par: Li, Zongzhao, et autres
Publié: (2025)

Separation Power of Equivariant Neural Networks
par: Pacini, Marco, et autres
Publié: (2024)

Fast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning
par: Zhang, Yuelin, et autres
Publié: (2025)

SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
par: Liu, Yang, et autres
Publié: (2023)

Beyond Loss Guidance: Using PDE Residuals as Spectral Attention in Diffusion Neural Operators
par: Sawhney, Medha, et autres
Publié: (2025)

From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
par: Li, Zhihao, et autres
Publié: (2026)

Intrinsic Lorentz Neural Network
par: Shi, Xianglong, et autres
Publié: (2026)

Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
par: Zheng, Zinan, et autres
Publié: (2024)

Learning Under Laws: A Constraint-Projected Neural PDE Solver that Eliminates Hallucinations
par: Singha, Mainak
Publié: (2025)

Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
par: Minartz, Koen, et autres
Publié: (2023)

Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networks
par: Jin, Dian, et autres
Publié: (2025)

Relative Geometry of Neural Forecasters: Linking Accuracy and Alignment in Learned Latent Geometry
par: Kucukahmetler, Deniz, et autres
Publié: (2026)

OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference
par: Wang, Zhuoyuan, et autres
Publié: (2026)

Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers
par: Zhou, Hang, et autres
Publié: (2024)

Generalized Reduction to the Isotropy for Flexible Equivariant Neural Fields
par: García-Castellanos, Alejandro, et autres
Publié: (2026)

Diagnosing Spectral Ceilings in Equivariant Neural Force Fields
par: Kim, Hyunmog
Publié: (2026)

Symmetry in the Wild: The Role of Equivariance in Neural Fluid Surrogates
par: Rygiel, Patryk, et autres
Publié: (2026)

Ambient Physics: Training Neural PDE Solvers with Partial Observations
par: Majid, Harris Abdul, et autres
Publié: (2026)

Generative Latent Neural PDE Solver using Flow Matching
par: Li, Zijie, et autres
Publié: (2025)

KO: Kinetics-inspired Neural Optimizer with PDE Simulation Approaches
par: Feng, Mingquan, et autres
Publié: (2025)

Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries
par: Li, Zhihao, et autres
Publié: (2024)

A Library for Learning Neural Operators
par: Kossaifi, Jean, et autres
Publié: (2024)

Recovering Physical Dynamics from Discrete Observations via Intrinsic Differential Consistency
par: Luo, Yuxiang, et autres
Publié: (2026)