7 papers · 1 filter
Local and Sparse Kernel Operator Learning for Incompressible Flows: An Operator Lagrange Formulation
Ramansh Sharma, Matthew Lowery, Houman Owhadi +1
We present a local and sparse kernel method for learning the solution operators of incompressible flows. It builds on our earlier property-preserving kernel method, whose global ou…
Property-Preserving Data Compression for Incompressible Flows
Andrey Pluzhnik, Ramansh Sharma, Martin Burtscher +1
We develop a method for lossy compression of incompressible (i.e., divergence-free) velocity fields that preserves zero divergence and, on periodic domains, periodicity. We fit the…
Decoding Extrahepatic Targeting of Lipid Nanoparticles with Interpretable Machine Learning
Asal Mehradfar, Mohammad Shahab Sepehri, Owen Antholine +4
Lipid nanoparticles (LNPs) have transformed RNA medicine, yet their clinical utility remains constrained by predominant hepatic accumulation after systemic administration. Redirect…
Anisotropic representations for E(3)-equivariant machine learning coarse-grained potentials
Varun Shankar, Emil Annevelink
Coarse-graining (CG) lowers the computational cost of atomistic simulations by representing groups of atoms as effective interaction sites, reducing the degrees of freedom of the s…
JetSCI: A Hybrid JAX-PETSc Framework for Scalable Differentiable Simulation
Alberto Cattaneo, M Keith Ballard, Robert M. Kirby +1
The rapid rise of scientific machine learning (SciML) has expanded the role of differentiable modeling, surrogate modeling, and data-driven constitutive laws in large-scale simulat…
Triplet Envelope Functions for increasing machine learning interatomic potential efficiency and stability
Emil Annevelink, Varun Shankar
Central to interatomic potential efficiency is the radial envelope function that enables linear scaling with computational cost by defining a local neighborhood of atoms. This has…
Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows
Ramansh Sharma, Matthew Lowery, Houman Owhadi +1
We present a novel property-preserving kernel-based operator learning method for incompressible flows governed by the incompressible Navier--Stokes equations. Traditional numerical…