7 papers
Neural Navigation Functions for Zero-Shot Generalizable Motion Planning
Benjamin D. Shaffer, Pei-An Hsieh, Brooks Kinch +2
We introduce Neural Navigation Functions (Neural-NF), a learned reactive navigation function capable of zero-shot transfer across unseen environment geometries. Neural-NF places da…
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…
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…
Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models
Benjamin Shaffer, Victoria Edwards, Brooks Kinch +2
Source localization in a complex flow poses a significant challenge for multi-robot teams tasked with localizing the source of chemical leaks or tracking the dispersion of an oil s…
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…
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…