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.
PIMILab
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DataDrivenParticleDynamics
DataDrivenParticleDynamics PublicCode for the paper "Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems".
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DataDrivenParticleDynamicsForLAMMPS
DataDrivenParticleDynamicsForLAMMPS PublicLAMMPS add-on for the paper "Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems".
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PIMILab.github.io
PIMILab.github.io PublicForked from mpa139/allanlab
Physics-Informed Machine Intelligence Laboratory website
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- cnwf Public
Code and examples for "Structure-Preserving Digital Twins via Conditional Neural Whitney Forms" by Kinch et al.
PIMILab/cnwf’s past year of commit activity - PIMILab.github.io Public Forked from mpa139/allanlab
Physics-Informed Machine Intelligence Laboratory website
PIMILab/PIMILab.github.io’s past year of commit activity - DataDrivenParticleDynamicsForLAMMPS Public
LAMMPS add-on for the paper "Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems".
PIMILab/DataDrivenParticleDynamicsForLAMMPS’s past year of commit activity - DataDrivenParticleDynamics Public
Code for the paper "Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems".
PIMILab/DataDrivenParticleDynamics’s past year of commit activity - .github Public
PIMILab/.github’s past year of commit activity
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