arXiv Machine Learning July 30, 2026 By Alejandro Garc\'ia-Castellanos, David R. Wessels, Nicky J. van den Berg, Remco Duits, Dani\"el M. Pelt, Erik J. Bekkers

Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces

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arXiv:2505. 16035v3 Announce Type: replace Abstract: We introduce Equivariant Neural Eikonal Solvers, a novel framework that integrates Equivariant Neural Fields (ENFs) with Neural Eikonal Solvers.

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benchmarks

arXiv Machine Learning

Jul 27

Latent PDE mapping for efficient physics-informed learning across geometries with limited data

arXiv:2607. 22215v1 Announce Type: new Abstract: In this study, we introduce latent PDE mapping, a broadly applicable physics-informed learning technique designed to enable efficient geometric generalization with sparse training data.

By Ingvild Askim Adde, Mary M. Maleckar, Gabriel Balaban

benchmarks

arXiv Machine Learning

Jun 17

Variational autoencoders with latent high-dimensional steady geometric flows for dynamics

arXiv:2410. 10137v5 Announce Type: replace Abstract: We develop Riemannian approaches to variational autoencoders (VAEs) for PDE-type ambient data with regularizing geometric latent dynamics, which we refer to as VAE-DLM, or VAEs with dynamical latent manifolds.

By Andrew Gracyk

diffusion

arXiv Machine Learning

Jul 3

Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs

arXiv:2603. 12676v3 Announce Type: replace Abstract: Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable.

By Zhangyong Liang, Huanhuan Gao

rag benchmarks

arXiv Machine Learning

Jun 15

EqCollide: Equivariant and Collision-Aware Deformable Objects Neural Simulator

arXiv:2506. 05797v2 Announce Type: replace Abstract: Simulating collisions of deformable objects is a fundamental yet challenging task due to the complexity of modeling solid mechanics and multi-body interactions.

By Qianyi Chen, Tianrun Gao, Chenbo Jiang, Tailin Wu

arXiv AI

Sep 1

Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs

arXiv:2608.28853v1 Announce Type: cross Abstract: Equivariant graph neural networks provide a principled way to model geometric systems, but efficient first-order architectures remain limited in how...

By Alessio Borgi, Mario Severino, Fabrizio Silvestri, Pietro Li\`o

arXiv Machine Learning

Jun 18

TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs

arXiv:2601. 20361v2 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) solve time-dependent partial differential equations (PDEs) by learning a mesh-free, differentiable solution that can be evaluated anywhere in space and time.

By Chen-Yang Dai, Che-Chia Chang, Te-Sheng Lin, Ming-Chih Lai, Chieh-Hsin Lai

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Latent PDE mapping for efficient physics-informed learning across geometries with limited data

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