Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs. - Justin M. Baker; Elena Cherkaev; Akil Narayan 001; Bao Wang 001 (CoRR, 2022)

Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs. - Justin M. Baker; Hedi Xia; Yiwei Wang; Elena Cherkaev; Akil Narayan 001; Long Chen; Jack Xin; Andrea L. Bertozzi; Stanley J. Osher; Bao Wang 001 (CoRR, 2022)

Implicit Graph Neural Networks: A Monotone Operator Viewpoint. - Justin M. Baker; Qingsong Wang; Cory D. Hauck; Bao Wang 001 (ICML, 2023)

Regularized Reduced Order Lippman-Schwinger-Lanczos Method for Inverse Scattering Problems in the Frequency Domain. - Justin M. Baker; Elena Cherkaev; Vladimir Druskin; Shari Moskow; Mikhail Zaslavsky (CoRR, 2023)

Learning Proper Orthogonal Decomposition of Complex Dynamics Using Heavy-ball Neural ODEs. - Justin M. Baker; Elena Cherkaev; Akil Narayan 001; Bao Wang 001 (J. Sci. Comput., 2023)

Monotone Operator Theory-Inspired Message Passing for Learning Long-Range Interaction on Graphs. - Justin M. Baker; Qingsong Wang; Martin Berzins; Thomas Strohmer; Bao Wang 001 (AISTATS, 2024)

Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks. - Shih-Hsin Wang; Yung-Chang Hsu; Justin M. Baker; Andrea L. Bertozzi; Jack Xin; Bao Wang 001 (ICLR, 2024)

An Explicit Frame Construction for Normalizing 3D Point Clouds. - Justin M. Baker; Shih-Hsin Wang; Tommaso de Fernex; Bao Wang 001 (ICML, 2024)

Learning to Control the Smoothness of Graph Convolutional Network Features. - Shih-Hsin Wang; Justin M. Baker; Cory D. Hauck; Bao Wang 001 (CoRR, 2024)

A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules. - Shih-Hsin Wang; Yuhao Huang; Justin M. Baker; Yuan-En Sun; Qi Tang; Bao Wang 001 (ICLR, 2025)

Regularized reduced order Lippmann-Schwinger-Lanczos method for inverse scattering problems in the frequency domain. - Justin M. Baker; Elena Cherkaev; Vladimir Druskin; Shari Moskow; Mikhail Zaslavsky (J. Comput. Phys., 2025)