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, alongside 7 external citations. Polarity classification is still indexing.
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A dimension-reduced HJB-FP stochastic control formulation for joint day-ahead bidding and real-time battery operation in PV systems with storage.
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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.
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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.