4 papers
Flow Matching for Efficient and Scalable Data Assimilation
Taos Transue, Bohan Chen, So Takao +1
Data assimilation (DA) estimates a dynamical system's state from noisy observations. Recent generative models like the ensemble score filter (EnSF) improve DA in high-dimensional n…
Improving Flow Matching by Aligning Flow Divergence
Yuhao Huang, Taos Transue, Shih-Hsin Wang +3
Conditional flow matching (CFM) stands out as an efficient, simulation-free approach for training flow-based generative models, achieving remarkable performance for data generation…
Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs
Shih-Hsin Wang, Yuhao Huang, Taos Transue +4
Graph neural networks (GNNs) have emerged as powerful tools for learning protein structures by capturing spatial relationships at the residue level. However, existing GNN-based met…
Learning Decentralized Swarms Using Rotation Equivariant Graph Neural Networks
Taos Transue, Bao Wang
The orchestration of agents to optimize a collective objective without centralized control is challenging yet crucial for applications such as controlling autonomous fleets, and su…