Posts by Collection

Two-way coupled Cloud-In-Cell modeling of non-isothermal particle-laden flows: a Subgrid Particle-Averaged Reynolds Stress-Equivalent (SPARSE) formulation

Published: August 01, 2019

Together with Gustaaf Jacobs and H.S. Udaykumar, at San Diego State University I worked on developing a two-way coupled Cloud-In-Cell (CIC) formulation for particle-laden flows that accounts for cloud size and subgrid-scale stresses using averaging techniques, and for cloud deformation using methods from continuum mechanics. It traces a physical cloud of particles as a point and distributes its influence on the carrier flow via a multivariate Gaussian distribution function.

Multilevel Monte Carlo

Published: September 01, 2020

Together with Daniel Tartakovsky and Sebastian Bosma, at Stanford University I developed an accelerated multilevel Monte Carlo approach via stratification/Latin hypercube sampling for the estimation of cumulative distribution functions.

Global sensitivity analysis and UQ for design accelerated by Deep Neural Networks (DNNs)

Published: December 01, 2021

Together with Daniel Tartakosvky, Eric Hall and Markos Katsoulakis, at Stanford University I worked on developing DNN approaches to accelerate mutual information-based sensitivity studies and uncertainty quantification for design of multiscale systems.

Accelerating Part-Scale Simulation in Liquid Metal Jet Additive Manufacturing via Operator Learning

Published: April 01, 2022

Together with Svyatoslav Korneev, Kyle Pietrzyk and Morad Behandish, at PARC, a Xerox Company I worked on developing an approach for speeding up quality prediction for 3D printed parts generated through liquid metal jetting by using operator learning to predict the mapping between initial and final states of droplet depositions in a moving subdomain approach.

Noise propagation in hybrid models of nonlinear systems: The Ginzburg-Landau equation

Published in Journal of Computational Physics, 2014

Abstract

Recommended citation: S. Taverniers, F.J. Alexander, and D.M. Tartakovsky. Noise propagation in hybrid models of nonlinear systems: The Ginzburg-Landau equation. J. Comp. Phys., 262:313-324 (2014). https://doi.org/10.1016/j.jcp.2014.01.015

Physics-based statistical learning approach to mesoscopic model selection

Published in Physical Review E, 2014

Abstract

Recommended citation: S. Taverniers, T.S. Haut, K. Barros, F.J. Alexander, and T. Lookman. Physics-based statistical learning approach to mesoscopic model selection. Phys. Rev. E 92, 053301 (2015). https://doi.org/10.1103/PhysRevE.92.053301

Conservative tightly-coupled simulations of stochastic multiscale systems

Published in Journal of Computational Physics, 2016

Abstract

Recommended citation: S. Taverniers, A.Y. Pigarov, and D.M. Tartakovsky. Conservative tightly-coupled simulations of stochastic multiscale systems. J. Comput. Phys., 313:400-414 (2016). https://doi.org/10.1016/j.jcp.2016.02.047

A tightly-coupled domain-decomposition approach for highly nonlinear stochastic multiphysics systems

Published in Journal of Computational Physics, 2017

Abstract

Recommended citation: S. Taverniers and D.M. Tartakovsky. A tightly-coupled domain-decomposition approach for highly nonlinear stochastic multiphysics systems. J. Comput. Phys., 330:884-901 (2017). https://doi.org/10.1016/j.jcp.2016.10.052

Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor

Published in Journal of Computational Physics, 2017

Abstract

Recommended citation: S. Taverniers and D.M. Tartakovsky. Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor. J. Comput. Phys., 348:139-150 (2017). https://doi.org/10.1016/j.jcp.2017.07.008

Two-way coupled Cloud-In-Cell modeling of non-isothermal particle-laden flows: A Subgrid Particle-Averaged Reynolds Stress-Equivalent (SPARSE) formulation

Published in Journal of Computational Physics, 2019

Abstract

Recommended citation: S. Taverniers, H.S. Udaykumar, and G.B. Jacobs. Two-way coupled Cloud-In-Cell modeling of non-isothermal particle-laden flows: A Subgrid Particle-Averaged Reynolds Stress-Equivalent (SPARSE) formulation. J. Comput. Phys., 390:595--618 (2019). https://doi.org/10.1016/j.jcp.2019.01.001

Estimation of distributions via multilevel Monte Carlo with stratified sampling

Published in Journal of Computational Physics, 2020

Abstract

Recommended citation: S. Taverniers and D.M. Tartakovsky. Estimation of distributions via multilevel Monte Carlo with stratified sampling. J. Comp. Phys., 419:109572 (2020). https://doi.org/10.1016/j.jcp.2020.109572

Accelerated multilevel Monte Carlo with kernel‐based smoothing and Latinized stratification

Published in Water Resources Research, 2020

Abstract

Recommended citation: S. Taverniers, S.B.M. Bosma, and D.M. Tartakovsky. Accelerated multilevel Monte Carlo with kernel‐based smoothing and Latinized stratification. Water Resources Res., 56, e2019WR026984 (2020). https://doi.org/10.1029/2019WR026984

GINNs: Graph-Informed Neural Networks for Multiscale Physics

Published in Journal of Computational Physics, 2021

Abstract

Recommended citation: E.J. Hall, S. Taverniers, M.A. Katsoulakis, and D.M. Tartakovsky. GINNs: Graph-Informed Neural Networks for Multiscale Physics. J. Comp. Phys., 433:110192 (2021). https://doi.org/10.1016/j.jcp.2021.110192

Mutual Information for explainable deep learning of multiscale systems

Published in Journal of Computational Physics, 2021

Abstract

Recommended citation: S. Taverniers, E.J. Hall, M.A. Katsoulakis, and D.M. Tartakovsky. Mutual Information for explainable deep learning of multiscale systems. J. Comp. Phys., 444:110551 (2021). https://doi.org/10.1016/j.jcp.2021.110551

A localized artificial diffusivity approach inspired by TVD schemes and its consistent application to compressible flows

Published in Stanford University Center for Turbulence Research Annual Research Briefs 2021, 2021

Abstract

Recommended citation: S. Mirjalili, S. Taverniers, H. Collis, M. Behandish, and A. Mani. A localized artificial diffusivity approach inspired by TVD schemes and its consistent application to compressible flows. CTR Annual Research Briefs., 169-182 (2021). http://web.stanford.edu/group/ctr/ResBriefs/2021/16_Mirjalili.pdf

Accelerating Part-Scale Simulation in Liquid Metal Jet Additive Manufacturing via Operator Learning

Published in AI-Based Design and Manufacturing (ADAM) workshop at the 36th AAAI Conference on Artificial Intelligence (2022), 2022

Abstract

Recommended citation: S. Taverniers, S. Korneev, K.M. Pietrzyk, and M. Behandish. Accelerating Part-Scale Simulation in Liquid Metal Jet Additive Manufacturing via Operator Learning. 36th AAAI Conference on Artificial Intelligence, (2022). https://arxiv.org/abs/2202.03665

Inverse asymptotic treatment: Capturing discontinuities in fluid flows via equation modification

Published in Journal of Computational Science, 2023

Abstract

Recommended citation: S. Mirjalili, S. Taverniers, H. Collis, M. Behandish, and A. Mani. Inverse asymptotic treatment: Capturing discontinuities in fluid flows via equation modification. J. Comput. Sci., 73:102141 (2023). https://doi.org/10.1016/j.jocs.2023.102141

A multi-physics compiler for generating numerical solvers from differential equations

Published in arXiv, 2023

Abstract

Recommended citation: J.T. Maxwell III, M. Behandish, and S. Taverniers. A multi-physics compiler for generating numerical solvers from differential equations (2023). https://arxiv.org/abs/2311.16404

A finite element method to compute the damping rate and frequency of oscillating fluids inside microfluidic nozzles

Published in Journal of Computational Physics, 2025

Abstract

Recommended citation: S. Taverniers, S. Korneev, C. Somarakis, M. Behandish, and A.J. Lew. A finite element method to compute the damping rate and frequency of oscillating fluids inside microfluidic nozzles. Int. J. Numer. Meth. Fl., 0:1-18 (2025). https://doi.org/10.1002/fld.5373

Cloud-In-Cell modeling of shocked particle-laden flows at a SPARSE cost

Published: April 22, 2017

Oral presentation at the 11th Southern California Flow Physics Symposium (So Cal Fluids XI).

A SPARSE-cost CIC model for particle-laden flows with two-way inter-phase coupling

Published: April 14, 2018

Oral presentation at the 12th Southern California Flow Physics Symposium (So Cal Fluids XII).

Two-way coupled cloud-in-cell modeling of non-isothermal particle-laden flows at a SPARSE cost

Published: April 08, 2019

Invited talk at Applied Mathematics seminar.

Statistical and physics-informed machine-learning approaches for multi-scale systems

Published: May 15, 2019

Invited dealer talk.

Graph-Informed Neural Networks

Published: March 22, 2021

Oral presentation at AAAI-MLPS 2021.

Accelerating Part-Scale Simulation in Liquid Metal Jet Additive Manufacturing via Operator Learning

Published: March 01, 2022

Oral presentation at AAAI-ADAM 2022.

An end-to-end multifidelity uncertainty quantification framework for CO2-induced seismicity prediction in carbon storage sites

Published: May 19, 2025

Oral presentation at InterPore 2025.