Geometry-physics confounding impairs PDE learning across varying domains

cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation
• Learning geometry-dependent lead-field operators for forward ECG modeling
• Neural Shape Operator Surrogates -- Expression Rate Bounds
• Diffeomorphic Latent Neural Operators for Data-Efficient Learning of Solutions to Partial Differential Equations
• Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks