Singular parameters and missing limits in neural PDE solvers
math.NA, cs.LG, cs.NA
Submitted: 2026-10-05
Updated: 2026-10-05
Sources
- Sparse RBF Networks for PDEs and nonlocal equations: function space theory, operator calculus, and training algorithms
- Affine symmetries and neural network identifiability
- An overview of condensation phenomenon in deep learning
- Convergence Guarantees for Gradient-Based Training of Neural PDE Solvers: From Linear to Nonlinear PDEs
- The Coercivity Gap in Neural PDE Solvers: Parameter Escape and Functional Convergence
Related papers
- Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
- A Neural-preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions
- Operator learning meets inverse problems: A probabilistic perspective
- Second-order consistency for learning chaotic dynamics via randomized Jacobian matching
- Windowed thinning and query complexity for the bouncy particle and Zigzag samplers
- Data-efficient Kernel Methods for Learning Hamiltonian Systems