Domain-decomposed Evolutional Deep Neural Network with Random Features for Transient Pressure Diffusion with Discontinuous and High-Contrast Coefficients

arXiv:2609.22349 · math.NA, cs.LG, cs.NA, math-ph, math.MP · Submitted 2026-09-17 · Read on arXiv

math.NA, cs.LG, cs.NA, math-ph, math.MP

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: 36 pages, 11 figures and 15 tables

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

Terminology

• Accuracy — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes

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