6 papers
SCOPE: Observation-Conditioned Full-Target Prediction for Sparse PDE Inference
Ruichen Xu, Siyao Wang, Fang Wan +8
Recovering complete physical fields from sparse observations is challenging because the measurements may not uniquely determine the underlying state. Diffusion-based PDE solvers ad…
PDE-OBS: Controlled Evaluation Across Observation Patterns
Ruichen Xu, Siyao Wang, Fang Wan +7
Physical-field reconstruction and forecasting depend on both measurement density and spatial layout, yet evaluation under a single observation pattern does not characterize perform…
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Ruichen Xu, Jingxiang Qu, Wenhan Gao +5
Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit neg…
Can Data-Driven Dynamics Reveal Hidden Physics? There Is A Need for Interpretable Neural Operators
Wenhan Gao, Jian Luo, Fang Wan +4
Recently, neural operators have emerged as powerful tools for learning mappings between function spaces, enabling data-driven simulations of complex dynamics. Despite their success…
GAGA: Gaussianity-Aware Gaussian Approximation for Efficient 3D Molecular Generation
Jingxiang Qu, Wenhan Gao, Ruichen Xu +1
Gaussian Probability Path based Generative Models (GPPGMs) generate data by reversing a stochastic process that progressively corrupts samples with Gaussian noise. Despite state-of…
RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation
Jingxiang Qu, Wenhan Gao, Jiaxing Zhang +4
3D Geometric Graph Neural Networks (GNNs) have emerged as transformative tools for modeling molecular data. Despite their predictive power, these models often suffer from limited i…