Noise propagation in hybrid models of nonlinear systems: The Ginzburg-Landau equation. - Søren Taverniers; Francis J. Alexander; Daniel M. Tartakovsky (J. Comput. Phys., 2014)

Conservative tightly-coupled simulations of stochastic multiscale systems. - Søren Taverniers; Alexander Y. Pigarov; Daniel M. Tartakovsky (J. Comput. Phys., 2016)

A tightly-coupled domain-decomposition approach for highly nonlinear stochastic multiphysics systems. - Søren Taverniers; Daniel M. Tartakovsky (J. Comput. Phys., 2017)

Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor. - Søren Taverniers; Daniel M. Tartakovsky (J. Comput. Phys., 2017)

Two-way coupled Cloud-In-Cell modeling of non-isothermal particle-laden flows: A Subgrid Particle-Averaged Reynolds Stress-Equivalent (SPARSE) formulation. - Søren Taverniers; H. S. Udaykumar; Gustaaf B. Jacobs (J. Comput. Phys., 2019)

Estimation of distributions via multilevel Monte Carlo with stratified sampling. - Søren Taverniers; Daniel M. Tartakovsky (J. Comput. Phys., 2020)

Graph-Informed Neural Networks. - Søren Taverniers; Eric Joseph Hall; Markos A. Katsoulakis; Daniel M. Tartakovsky (AAAI Spring Symposium: MLPS, 2021)

GINNs: Graph-Informed Neural Networks for multiscale physics. - Eric Joseph Hall; Søren Taverniers; Markos A. Katsoulakis; Daniel M. Tartakovsky (J. Comput. Phys., 2021)

Mutual information for explainable deep learning of multiscale systems. - Søren Taverniers; Eric Joseph Hall; Markos A. Katsoulakis; Daniel M. Tartakovsky (J. Comput. Phys., 2021)

Accelerating Part-Scale Simulation in Liquid Metal Jet Additive Manufacturing via Operator Learning. - Søren Taverniers; Svyatoslav Korneev; Kyle M. Pietrzyk; Morad Behandish (CoRR, 2022)

Inverse asymptotic treatment: Capturing discontinuities in fluid flows via equation modification. - Shahab Mirjalili; Søren Taverniers; Henry Collis; Morad Behandish; Ali Mani (J. Comput. Sci., 2023)