most citedAccelerating PDE-constrained Inverse Solutions with Deep Learning and Reduced Order Models

16 citations · 23 across the 4 of their papers we have counts for

collaborators

5 papers

physics.comp-ph2020

Massively Parallel Transport Sweeps on Meshes with Cyclic Dependencies

Jan I C Vermaak, Jean C Ragusa, Jim E Morel

When solving the first-order form of the linear Boltzmann equation, a common misconception is that the matrix-free computational method of ``sweeping the mesh", used in conjunction…

physics.comp-ph201916 cited

Accelerating PDE-constrained Inverse Solutions with Deep Learning and Reduced Order Models

Sheroze Sheriffdeen, Jean C. Ragusa, Jim E. Morel +2

Inverse problems are pervasive mathematical methods in inferring knowledge from observational and experimental data by leveraging simulations and models. Unlike direct inference me…

physics.comp-ph2019

Parallel Approximate Ideal Restriction Multigrid for Solving the S Transport Equations

Joshua Hanophy, Ben S. Southworth, Ruipeng Li +2

The computational kernel in solving the transport equations is the parallel sweep, which corresponds to directly inverting a block lower triangular linear system that arises…

physics.comp-ph20193 cited

Nonlinear Diffusion Acceleration of the Least-Squares Transport Equation in Geometries with Voids

Hans Hammer, Jim Morel, Yaqi Wang

In this paper we show the extension of the Nonlinear-Diffusion Acceleration (NDA) to geometries containing small voids using a weighted least-squares (WLS) high order equation. Eve…

physics.comp-ph20194 cited

A Weighted Least-Squares Transport Equation Compatible with Source Iteration and Voids

Hans Hammer, Jim Morel, Yaqi Wang

Least-squares (LS) forms of the transport equation can circumvent the void problems of other second order forms, but are almost always non-conservative. Additionally, the standard…