A physics-informed GNN-based foundation model for AC-OPF, pretrained across 54 grid topologies (500-4,000 buses), achieving 2.45% zero-shot generation-cost error on 10,000-bus systems.
NewSee LICENSE fileAC-OPFGraph Neural NetworkPower System Optimization
A physics-informed neural operator learns the map between function spaces in magnetohydrodynamic simulations, enabling efficient state inference for magnetic reconnection at any parameter and time step, significantly reducing computational cost at high Lundquist numbers.
See LICENSE fileMagnetohydrodynamicsMagnetic ReconnectionNeural Operator
A systematic review of physics-informed machine learning applications in electricity system design, monitoring, and control, focusing on hybrid architectures and physics constraint embedding methods.
See LICENSE filePhysics-Informed LearningElectricity SystemsElectromagnetic Simulation
