5 papers · 1 filter
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…
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…
Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems
Quercus Hernandez, Max Win, Thomas C. O'Connor +2
Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bul…
Physics-informed sensor coverage through structure preserving machine learning
Benjamin David Shaffer, Brooks Kinch, Joseph Klobusicky +2
We present a machine learning framework for adaptive source localization in which agents use a structure-preserving digital twin of a coupled hydrodynamic-transport system for real…
Structure-Preserving Digital Twins via Conditional Neural Whitney Forms
Brooks Kinch, Benjamin Shaffer, Elizabeth Armstrong +3
We present a framework for constructing real-time digital twins based on structure-preserving reduced finite element models conditioned on a latent variable Z. The approach uses co…