MD-PNOP recasts parameter-induced operator differences as source terms to enable single-configuration neural operator training for extrapolation and acceleration of parametric PDE solvers.
Three ways to solve partial di fferential equa- tions with neural networks - A review,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
A dimension-reduced HJB-FP stochastic control formulation for joint day-ahead bidding and real-time battery operation in PV systems with storage.
citing papers explorer
-
MD-PNOP: Equation-Recast Neural Operators for Minimal-Data Extrapolation and PDE Solver Acceleration
MD-PNOP recasts parameter-induced operator differences as source terms to enable single-configuration neural operator training for extrapolation and acceleration of parametric PDE solvers.
-
State constrained stochastic optimal control of a PV system with battery storage via Fokker-Planck and Hamilton-Jacobi-Bellman equations
A dimension-reduced HJB-FP stochastic control formulation for joint day-ahead bidding and real-time battery operation in PV systems with storage.