5 papers
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…
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…
Rootfinding and Optimization Techniques for Solving Nonlinear Systems of Equations Arising from Cohesive Zone Models
Alberto Cattaneo, Varun Shankar, M. Keith Ballard
While approaches to model the progression of fracture have received significant attention, methods to find the solution to the associated nonlinear equations have not. In general,…