3 papers
eess.IV2026
Physics-Informed Hemodynamic Modeling for Data-Free Prediction and Sparse-Data Assimilation
Xi Chen, Jianchuan Yang, Hongde Li +5
Clinical decision-making for coronary intervention relies mainly on angiography and fractional flow reserve (FFR). However, angiography is two-dimensional and lacks depth informati…
cs.LG2026
FEM-Informed Hypergraph Neural Networks for Efficient Elastoplasticity
Jianchuan Yang, Xi Chen, Jidong Zhao
Graph neural networks (GNNs) naturally align with sparse operators and unstructured discretizations, making them a promising paradigm for physics-informed machine learning in compu…
cs.LG2025
Solved in Unit Domain: JacobiNet for Differentiable Coordinate-Transformed PINNs
Xi Chen, Jianchuan Yang, Junjie Zhang +6
Physics-Informed Neural Networks (PINNs) offer a powerful framework for solving PDEs by embedding physical laws into the learning process. However, when applied to domains with irr…