H^{-1} norm equivalence to expected squared evaluations on domain-dependent random test functions enables SV-PINNs that recover accurate solutions to challenging second-order elliptic PDEs faster than standard PINNs.
American mathematical society
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
The authors prove a population L2 stability estimate and finite-sample certificate for one policy-evaluation step in a neural HJB solver with learned dynamics, plus multi-step propagation through greedy improvement, with experiments on high-dimensional control tasks.
citing papers explorer
-
Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks
H^{-1} norm equivalence to expected squared evaluations on domain-dependent random test functions enables SV-PINNs that recover accurate solutions to challenging second-order elliptic PDEs faster than standard PINNs.
-
Stabilized neural Hamilton--Jacobi--Bellman solvers: Error analysis and applications in model-based reinforcement learning
The authors prove a population L2 stability estimate and finite-sample certificate for one policy-evaluation step in a neural HJB solver with learned dynamics, plus multi-step propagation through greedy improvement, with experiments on high-dimensional control tasks.
- Global in time solutions to stochastic reaction-diffusion systems with superlinear reactions satisfying a triangular control of mass