37 citations · 85 across the 5 of their papers we have counts for
5 papers
Inverse asymptotic treatment: capturing discontinuities in fluid flows via equation modification
Shahab Mirjalili, Søren Taverniers, Henry Collis +2
A major challenge in developing accurate and robust numerical solutions to multi-physics problems is to correctly model evolving discontinuities in field quantities, which manifest…
Accelerating Part-Scale Simulation in Liquid Metal Jet Additive Manufacturing via Operator Learning
Søren Taverniers, Svyatoslav Korneev, Kyle M. Pietrzyk +1
Predicting part quality for additive manufacturing (AM) processes requires high-fidelity numerical simulation of partial differential equations (PDEs) governing process multiphysic…
Mutual Information for Explainable Deep Learning of Multiscale Systems
Søren Taverniers, Eric J. Hall, Markos A. Katsoulakis +1
Timely completion of design cycles for complex systems ranging from consumer electronics to hypersonic vehicles relies on rapid simulation-based prototyping. The latter typically i…
GINNs: Graph-Informed Neural Networks for Multiscale Physics
Eric J. Hall, Søren Taverniers, Markos A. Katsoulakis +1
We introduce the concept of a Graph-Informed Neural Network (GINN), a hybrid approach combining deep learning with probabilistic graphical models (PGMs) that acts as a surrogate fo…
Estimation of distributions via multilevel Monte Carlo with stratified sampling
Søren Taverniers, Daniel M. Tartakovsky
We design and implement a novel algorithm for computing a multilevel Monte Carlo (MLMC) estimator of the cumulative distribution function of a quantity of interest in problems with…