Topic 1：Differentiable Neural Solver and GPU Computing
Aiming to develop next-gen computational modeling framework by combining classic numerical method, deep learning techniques, and data via differentiable programming and GPU computing — enabling efficient forward/inverse modeling and data assimilation.
Topic 2：Advance deep learning techniques
We are pushing the boundary of deep learning architecture, data-driven techniques for learning spatiotemporal system – graph neural networks, sequence2sequence networks, transformer, diffusion models et.
Topic 3：Physics-informed, PDE-regularized Machine/Deep Learning
Using residual of governing PDEs to regularized or drive machine/deep learning model training, e.g., physics-informed neural networks, B-PINN, physics-informed CNN, and FEM-GNN etc.
