Ionut Farcas

Hello and welcome to my profile! My name is Ionut Farcas, and I am an assistant professor in the Department of Mathematics and Division of Computational Modeling and Data Analytics at Virginia Tech.

Mathematical modeling and simulation are indispensable tools for understanding the natural world, empowering us to make informed decisions and drive innovation across diverse fields. Two contemporary examples are from aerospace propulsion where rotating detonation rocket engine simulations are used in support of the design of next-generation rocket engines, and fusion plasma physics where simulations are essential for quantifying and predicting turbulent transport in magnetic confinement experiments.

To address these challenges, my research is dedicated to enabling or accelerating predictions and decision-making, and increasing the robustness of numerical models in complex, real-world applications. My work blends scientific computing, high-performance computing, and computational physics, with a particular emphasis on scientific machine learning, reduced and surrogate modeling, uncertainty quantification, and sparse grid and multi-fidelity methods.

Beyond research, I am also passionate about inspiring the next generation of computational scientists through teaching and mentorship.

Please feel free to reach out to me!

Publications

Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine

Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine

Ionut-Gabriel Farcas, Rayomand P. Gundevia, R. Munipalli, Karen E. Willcox

arXiv.org 2024

ABSHTMLPDF
Advanced surrogate model for electron-scale turbulence in tokamak pedestals

Advanced surrogate model for electron-scale turbulence in tokamak pedestals

Ionut-Gabriel Farcas, Gabriele Merlo, Frank Jenko

Journal of Plasma Physics 2024

ABSHTMLPDF
Scientific machine learning based reduced-order models for plasma turbulence simulations

Scientific machine learning based reduced-order models for plasma turbulence simulations

Constatin Gahr, Ionut-Gabriel Farcas, Frank Jenko

Physics of Plasmas 2024

ABSHTMLPDF
Domain Decomposition for Data-Driven Reduced Modeling of Large-Scale Systems

Domain Decomposition for Data-Driven Reduced Modeling of Large-Scale Systems

Ionut-Gabriel Farcas, Rayomand P. Gundevia, R. Munipalli, Karen E. Willcox

AIAA Journal 2023

ABSHTMLPDF
Parametric non-intrusive reduced-order models via operator inference for large-scale rotating detonation engine simulations

Parametric non-intrusive reduced-order models via operator inference for large-scale rotating detonation engine simulations

Ionut-Gabriel Farcas, Rayomand P. Gundevia, R. Munipalli, K. Willcox

AIAA SCITECH 2023 Forum 2023

ABS
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification

Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification

Ionut-Gabriel Farcas, B. Peherstorfer, T. Neckel, F. Jenko, H. Bungartz

Computer Methods in Applied Mechanics and Engineering 2022

ABSPDF
On filtering in non-intrusive data-driven reduced-order modeling

On filtering in non-intrusive data-driven reduced-order modeling

Ionut-Gabriel Farcas, R. Munipalli, K. Willcox

AIAA AVIATION 2022 Forum 2022

ABS

A general framework for quantifying uncertainty at scale

Ionut-Gabriel Farcas, G. Merlo, F. Jenko

Communications Engineer 2022

ABSPDF
Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis

Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis

J. Konrad, Ionut-Gabriel Farcas, B. Peherstorfer, A. Siena, F. Jenko, T. Neckel, H. Bungartz

Journal of Computational Physics 2021

ABSPDF
Reduced operator inference for nonlinear partial differential equations

Reduced operator inference for nonlinear partial differential equations

E. Qian, Ionut-Gabriel Farcas, K. Willcox

SIAM Journal on Scientific Computing 2021

ABSPDF

Turbulence suppression by energetic particles: a sensitivity-driven dimension-adaptive sparse grid framework for discharge optimization

Ionut-Gabriel Farcas, A. Siena, F. Jenko

Nuclear Fusion 2021

ABSPDF
Multilevel adaptive sparse Leja approximations for Bayesian inverse problems

Multilevel adaptive sparse Leja approximations for Bayesian inverse problems

Ionut-Gabriel Farcas, J. Latz, E. Ullmann, T. Neckel, H. Bungartz

SIAM Journal on Scientific Computing 2019

ABSPDF
Sensitivity-driven adaptive sparse stochastic approximations in plasma microinstability analysis

Sensitivity-driven adaptive sparse stochastic approximations in plasma microinstability analysis

Ionut-Gabriel Farcas, T. Görler, H. Bungartz, F. Jenko, T. Neckel

Journal of Computational Physics 2018

ABSPDF
Nonintrusive Uncertainty Analysis of Fluid-Structure Interaction with Spatially Adaptive Sparse Grids and Polynomial Chaos Expansion

Nonintrusive Uncertainty Analysis of Fluid-Structure Interaction with Spatially Adaptive Sparse Grids and Polynomial Chaos Expansion

Ionut-Gabriel Farcas, B. Uekermann, T. Neckel, H. Bungartz

SIAM Journal on Scientific Computing 2018

ABS
E-health decision support system for differential diagnosis

E-health decision support system for differential diagnosis

R. Cucu, C. Avram, A. Astilean, Ionut-Gabriel Farcas, J. Machado

International Conference on Automation, Quality and Testing, Robotics 2014

ABS

Sensitivity-driven dimension-adaptive sparse stochastic approximations in linear gyrokinetics

Ionut-Gabriel Farcas, T. Goerler, H. Bungartz, F. Jenko, T. Neckel

Multilevel Adaptive Stochastic Collocation with Dimensionality Reduction

Ionut-Gabriel Farcas, Paul-Cristian Sarbu, H. Bungartz, T. Neckel, B. Uekermann

ABS

En Route to High-Performance Discharges: Insights and Guidance from High-Realism Gyrokinetics

T. Görler, A. Siena, H. Doerk, T. Happel, S. Freethy, Ionut-Gabriel Farcas, A. Navarro, R. Bilato, A. Bock, J. Citrin, G. Conway, A. Creely, P. Hennequin, F. Jenko, T. Johnson, C. Lechte, T. Neckel, E. Poli, M. Schneider, E. Sonnendruecker, J. Stober, A. White, ASDEX-Upgrade-Team, JET-Contributors

Comparison of Numerical Methods in Uncertainty Quantification

Comparison of Numerical Methods in Uncertainty Quantification

Ionut-Gabriel Farcas

GI-Jahrestagung 2014