activity
20182023
most citedConstraint-Aware Neural Networks for Riemann Problems

72 citations · 73 across the 4 of their papers we have counts for

collaborators

5 papers

math.NA2023

A Multiscale Method for Two-Component, Two-Phase Flow with a Neural Network Surrogate

Jim Magiera, Christian Rohde

Understanding the dynamics of phase boundaries in fluids requires quantitative knowledge about the microscale processes at the interface. We consider the sharp-interface motion of…

math.NA2022★ 1 cited

A Molecular-Continuum Multiscale Model for Inviscid Liquid-Vapor Flow with Sharp Interfaces

Jim Magiera, Christian Rohde

The dynamics of compressible liquid-vapor flow depends sensitively on the microscale behavior at the phase boundary. We consider a sharp-interface approach, and propose a multiscal…

cs.CG2021

An Interface Preserving Moving Mesh in Multiple Space Dimensions

Maria Alkämper, Jim Magiera, Christian Rohde

An interface preserving moving mesh algorithm in two or higher dimensions is presented. It resolves a moving -dimensional manifold directly within the -dimensional mesh,…

physics.comp-ph2019★ 72 cited

Constraint-Aware Neural Networks for Riemann Problems

Jim Magiera, Deep Ray, Jan S. Hesthaven +1

Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates. In many applications physical…

math.NA2018

A Particle-based Multiscale Solver for Compressible Liquid-Vapor Flow

Jim Magiera, Christian Rohde

To describe complex flow systems accurately, it is in many cases important to account for the properties of fluid flows on a microscopic scale. In this work, we focus on the descri…