Education

• Ph.D. in Engineering Physics, University of California San Diego (UCSD), USA, 2016 (GPA: 4.0/4.0)
• M.S. in Engineering Physics, University of California San Diego (UCSD), USA, 2012
• M.S. in Physics, Katholieke Universiteit Leuven, Belgium, 2006 (Cum Laude)

Selected Awards and Honors

• Intelligent Systems Lab (ISL) Peer Award, 2023
  Future Concepts Division, SRI International

• Intelligent Systems Lab (ISL) Individual Award, 2022
  PARC, a Xerox Company

• Intelligent Systems Lab (ISL) Team Award, 2022
  PARC, a Xerox Company

• Nomination for Chancellor’s Dissertation Medal by MAE Department, 2017
  University of California San Diego

Work Experience in Industry

• Research Scientist, Future Concepts Division, SRI International, May 2023-June 2024

• Member of Research Staff, PARC, a Xerox Company, October 2020-April 2023

Work Experience in Academia and Government Labs

• Physical Science Research Scientist, Stanford University, September 2024-Present
  • Research topics: Predictive computational modeling and deep learning accelerated uncertainty quantification for induced seismicity during $\mathrm{CO}_2$ injection into the subsurface
  • Supervisor: Prof. Hamdi A. Tchelepi
  • Affiliation: Energy Science & Engineering Department
• Postdoctoral Scholar, Stanford University, September 2018-September 2020
  • Research topics: Predictive computational modeling and deep learning accelerated sensitivity analysis/uncertainty quantification of multiphysics systems including subsurface flows and energy storage systems
  • Supervisor: Prof. Daniel M. Tartakovsky
  • Affiliation: Department of Energy Resources Engineering
• Research Specialist I, San Diego State University Research Foundation, October 2016-July 2018
  • Research topic: Modeling and efficient simulation of shocked particle-laden flows
  • Supervisor: Prof. Gustaaf B, Jacobs
  • Affiliation: Department of Aerospace Engineering
• Graduate Student Researcher, Los Alamos National Laboratory (LANL), August 2014-June 2015
  • Research topic: Mesoscopic model selection through physics-based machine learning
  • Supervisor: Dr. Turab Lookman
  • Affiliation: Department of Mechanical and Aerospace Engineering, University of California San Diego (UCSD) (internship at LANL was part of my PhD at UCSD)
• Graduate Student Researcher,University of California San Diego (UCSD), September 2011-June 2014
  • Research topic: Development of nonintrusive approaches for multiphysics problems with random noise
  • Supervisor: Prof. Daniel M. Tartakovsky
  • Affiliation: Department of Mechanical and Aerospace Engineering, University of California San Diego (UCSD)

Publications

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

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).

Physics-based statistical learning approach to mesoscopic model selection

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).

Conservative tightly-coupled simulations of stochastic multiscale systems

S. Taverniers, A.Y. Pigarov, and D.M. Tartakovsky. Conservative tightly-coupled simulations of stochastic multiscale systems. J. Comput. Phys., 313:400-414 (2016).

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

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).

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

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).

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

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).

Estimation of distributions via multilevel Monte Carlo with stratified sampling

S. Taverniers and D.M. Tartakovsky. Estimation of distributions via multilevel Monte Carlo with stratified sampling. J. Comp. Phys., 419:109572 (2020).

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

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).

GINNs: Graph-Informed Neural Networks for Multiscale Physics

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).

Mutual Information for explainable deep learning of multiscale systems

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).

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

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).

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

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).

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

S. Mirjalili, S. Taverniers, H. Collis, M. Behandish, and A. Mani. Inverse asymptotic treatment: Capturing discontinuities in fluid flows via equation modification. <iJ. Comput. Sci., 73:102141 (2023).

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

J.T. Maxwell III, M. Behandish, and S. Taverniers. A multi-physics compiler for generating numerical solvers from differential equations (2023).

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

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. <iInt. J. Numer. Meth. Fl., 0:1-18 (2025).

Talks

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

April 22, 2017

Conference talk at So Cal Fluids XI at the University of California San Diego, La Jolla, CA, USA

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

April 14, 2018

Conference talk at So Cal Fluids XII at the University of Southern California, Los Angeles, CA, USA

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

April 08, 2019

Talk at University of California Santa Cruz, Santa Cruz, California, USA

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

May 15, 2019

Talk at PARC, a Xerox Company, Palo Alto, California, USA

Graph-Informed Neural Networks

March 22, 2021

Conference talk at AAAI-MLPS, Stanford, California, USA (held virtually)

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

March 01, 2022

Conference talk at AAAI-ADAM, Vancouver, B.C., Canada (held virtually)

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

May 19, 2025

Conference talk at InterPore 2025, Albuquerque, New Mexico, USA