9 citations · 9 across the 5 of their papers we have counts for
5 papers
A meshfree exterior calculus for generalizable and data-efficient learning of physics from point clouds
Benjamin D. Shaffer, Brooks Kinch, M. Ani Hsieh +1
We introduce a meshfree exterior calculus (MEEC) for learning structure-preserving descriptions of physics on point clouds, and use it to build MEEC-Net, a data-efficient surrogate…
Learned Lagrangian Models of PDEs via Euler-Lagrange Residual Minimization
Lyra Zhornyak, Eric Forgoston, M. Ani Hsieh
We present the first method to directly use a learned continuous Lagrangian to forecast the dynamics of systems governed by partial differential equations, exploiting the inherent…
Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs
Benjamin D. Shaffer, Shawn Koohy, Brooks Kinch +2
We aim to develop physics foundation models for science and engineering that provide real-time solutions to Partial Differential Equations (PDEs) which preserve structure and accur…
Spectrally Informed Learning of Fluid Flows
Benjamin D. Shaffer, Jeremy R. Vorenberg, M. Ani Hsieh
Accurate and efficient fluid flow models are essential for applications relating to many physical phenomena including geophysical, aerodynamic, and biological systems. While these…
Drone-based Object Counting by Spatially Regularized Regional Proposal Network
Meng-Ru Hsieh, Yen-Liang Lin, Winston H. Hsu
Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e.g., high-level understandi…