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
Develops a droop-aware extension of GridFM foundation model for power systems, embedding droop gains and deadband parameters as node features for control-aware AC power flow analysis.
See LICENSE filePower SystemsPower FlowFoundation Model
A general-purpose machine-learned interatomic potential built on an equivariant graph neural network, trained on 150k public inorganic crystals, that runs stable molecular dynamics out of the box for solids, liquids, gases and reactions, and can be fine-tuned to ab initio accuracy with only a handful of configurations.
MIT (code) / arXiv preprintMolecular DynamicsML PotentialEquivariant GNN
ETH Zurich's PDE foundation model and downstream fine-tuning suite, using semigroup augmentation to mine abundant training pairs, covering 15 downstream PDE tasks.
See LICENSE fileFoundation ModelNeural OperatorDownstream Tasks
