FOSLS-deRhaNN: native de Rham neural classes for H(div) and H(curl) with applications to first-order system least-squares neural network methods for partial differential equations
math.NA, cs.LG, cs.NA
Submitted: 2026-10-06
Updated: 2026-10-06
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
- 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
- Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problems