Skip to content
@PIMILab

PIMILab

PIMILab
README.md

Welcome to the Physics-Informed Machine Intelligence Laboratory

We are a research group at University of Pennsylvania led by Prof. Nat Trask.

We develop machine learning methods grounded in the structure of physics and geometry. By combining tools from geometric mechanics, exterior calculus, and variational modeling with modern AI architectures, we create interpretable and reliable models for complex physical systems in high-consequence engineering settings. Our work spans simulation, scientific discovery, and data-driven inference across multiscale and multiphysics domains including energy, material discovery, fusion, and soft matter. Our groups primary focus is on the construction of learning frameworks that encode physical principles by construction in neural architectures so that models provide the stability, physical realizability, and performance guarantees that are crucial to traditional modeling and simulation but lacking in contemporary machine learned models.

Popular repositories Loading

  1. DataDrivenParticleDynamics DataDrivenParticleDynamics Public

    Code for the paper "Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems" published in Proceedings of the National Academy of Science…

    Python 12 3

  2. DataDrivenParticleDynamicsForLAMMPS DataDrivenParticleDynamicsForLAMMPS Public

    LAMMPS add-on for the paper "Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems".

    C++ 4

  3. cnwf cnwf Public

    Code and examples for "Structure-Preserving Digital Twins via Conditional Neural Whitney Forms" by Kinch et al.

    Python 3 2

  4. Geo-NeW Geo-NeW Public

    General Geometry Neural Whitney Forms for PDEs on complex geometries

    Python 3 2

  5. PIMILab.github.io PIMILab.github.io Public

    Forked from mpa139/allanlab

    Physics-Informed Machine Intelligence Laboratory website

    SCSS 1

  6. hnode hnode Public

    Code and examples for "A hybridizable neural time integrator for stable autoregressive forecasting"

    Jupyter Notebook 1

Repositories

Showing 9 of 9 repositories

People

This organization has no public members. You must be a member to see who’s a part of this organization.

Top languages

Loading…

Most used topics

Loading…