We build Physics Foundation Models, mastering the fundamental laws governing reality. We believe this deep Physics AI—distinct from Physical AI—is not just the next wave, but a crucial key to unlocking Artificial General Intelligence.
Research Highlights
Learnable Orthogonal Decomposition for Non-Regressive Prediction for PDE (CIKM, 2025)Learning multiphase and multiphysics system with decoupled state space model (ICLR 2025 Workshop: XAI4Science, 2025)Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks(LoG, 2024)Topology-Informed Graph Transformer (ICML GRaM workshop, 2024)Topological Deep Learning Challenge 2024: Beyond the Graph Domain ChallengeCycle to clique (cy2c) graph neural network: A sight to see beyond neighborhood aggregation (ICLR, 2023)The pwlr graph representation: A persistent weisfeiler-lehman scheme with random walks for graph classification (ICML TAG workshop, 2022)Two-layer hydrodynamic network model for redox flow battery stack with flow field design (International Journal of Heat and Mass Transfer, 2023)Impedance-based capacity estimation for lithium-ion batteries using generative adversarial network (Applied Energy, 2022)Multiple parameter identification using genetic algorithm in vanadium redox flow batteries (Journal of Power Sources, 2020)
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2. x:
The Unknown, new challenges, infinite future possibilities
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