7 papers
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…
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…
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…
A Unified Framework for Efficient Kernel and Polynomial Interpolation
M. Belianovich, G. E. Fasshauer, A. Narayan +1
We present a unified interpolation scheme that combines compactly-supported positive-definite kernels and multivariate polynomials. This unified framework generalizes interpolation…
Ensemble and Mixture-of-Experts DeepONets For Operator Learning
Ramansh Sharma, Varun Shankar
We present a novel deep operator network (DeepONet) architecture for operator learning, the ensemble DeepONet, that allows for enriching the trunk network of a single DeepONet with…