{"total":20,"rows":[{"id":"2410.02698","title":"Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups","created":"2024-10-03","cited_by_count":24,"area":null},{"id":"2405.15603","title":"Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks","created":"2024-05-24","cited_by_count":21,"area":null},{"id":"2409.18426","title":"Dual Cone Gradient Descent for Training Physics-Informed Neural Networks","created":"2024-09-27","cited_by_count":21,"area":null},{"id":"2505.12149","title":"Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization","created":"2025-05-17","cited_by_count":13,"area":null},{"id":"2506.10973","title":"Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning","created":"2025-06-12","cited_by_count":12,"area":null},{"id":"2604.05230","title":"Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks","created":"2026-04-06","cited_by_count":11,"area":null},{"id":"2412.10782","title":"ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning","created":"2024-12-14","cited_by_count":7,"area":null},{"id":"2505.21404","title":"Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks","created":"2025-05-27","cited_by_count":6,"area":null},{"id":"2507.10835","title":"Functional Neural Wavefunction Optimization","created":"2025-07-14","cited_by_count":3,"area":null},{"id":"2502.00373","title":"Generalized Lie Symmetries in Physics-Informed Neural Operators","created":"2025-02-01","cited_by_count":2,"area":null},{"id":"2503.00755","title":"Riemann Tensor Neural Networks: Learning Conservative Systems with Physics-Constrained Networks","created":"2025-03-02","cited_by_count":2,"area":null},{"id":"2508.21022","title":"A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms","created":"2025-08-28","cited_by_count":2,"area":null},{"id":"2604.15554","title":"Natural gradient descent with momentum","created":"2026-04-16","cited_by_count":2,"area":null},{"id":"2505.11638","title":"Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra","created":"2025-05-16","cited_by_count":1,"area":null},{"id":"2510.02683","title":"Can Data-Driven Dynamics Reveal Hidden Physics? There Is A Need for Interpretable Neural Operators","created":"2025-10-03","cited_by_count":1,"area":null},{"id":"2512.18059","title":"Approximation and learning with compositional tensor trains","created":"2025-12-19","cited_by_count":1,"area":null},{"id":"2512.20566","title":"Random Gradient-Free Optimization in Infinite Dimensional Spaces","created":"2025-12-23","cited_by_count":1,"area":null},{"id":"2609.36216","title":"Preconditioned Physics-Informed Neural Operator Training","created":"2026-09-28","cited_by_count":1,"area":null},{"id":"2602.00099","title":"Gauss-Newton Natural Gradient Descent for Shape Learning","created":"2026-01-24","cited_by_count":0,"area":null},{"id":"2607.22004","title":"Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers","created":"2026-07-24","cited_by_count":0,"area":null}],"next_cursor":null}