Ionut-Gabriel Farcas

According to our database, Ionut-Gabriel Farcas authored at least 18 papers between 2014 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Convolution Operator Network for Forward and Inverse Problems (FI-Conv): Application to Plasma Turbulence Simulations.

Xingzhuo Chen, Anthony Poole, Ionut-Gabriel Farcas, David R. Hatch, Ulisses Braga-Neto

CoRR, February, 2026

Fast prediction of plasma instabilities with sparse-grid-accelerated optimized dynamic mode decomposition.

Kevin Gill, Ionut-Gabriel Farcas, Silke Glas, Benjamin J. Faber

J. Comput. Phys., 2026

2025

Machine Learning for Electron-Scale Turbulence Modeling in W7-X.

Ionut-Gabriel Farcas, Don Lawrence Carl Agapito Fernando, Alejandro Bañón Navarro, Gabriele Merlo, Frank Jenko

CoRR, November, 2025

Real-time prediction of plasma instabilities with sparse-grid-accelerated optimized dynamic mode decomposition.

Kevin Gill, Ionut-Gabriel Farcas, Silke Glas, Benjamin J. Faber

CoRR, July, 2025

A parallel implementation of reduced-order modeling of large-scale systems.

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

CoRR, April, 2025

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

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

Comput. Phys. Commun., 2025

2024

Learning physics-based reduced models from data for the Hasegawa-Wakatani equations.

Constatin Gahr, Ionut-Gabriel Farcas, Frank Jenko

CoRR, 2024

2023

Improving the accuracy and scalability of large-scale physics-based data-driven reduced modeling via domain decomposition.

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

CoRR, 2023

2022

Reduced Operator Inference for Nonlinear Partial Differential Equations.

Elizabeth Qian, Ionut-Gabriel Farcas, Karen Willcox

SIAM J. Sci. Comput., 2022

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

Julia Konrad, Ionut-Gabriel Farcas, Benjamin Peherstorfer, Alessandro Di Siena, Frank Jenko, Tobias Neckel, Hans-Joachim Bungartz

J. Comput. Phys., 2022

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

Ionut-Gabriel Farcas, Benjamin Peherstorfer, Tobias Neckel, Frank Jenko, Hans-Joachim Bungartz

CoRR, 2022

A general framework for quantifying uncertainty at scale and its application to fusion research.

Ionut-Gabriel Farcas, Gabriele Merlo, Frank Jenko

CoRR, 2022

2020

Context-aware Model Hierarchies for Higher-dimensional Uncertainty Quantification (Kontextsensitive Modellhierarchien für Quantifizierung der höherdimensionalen Unsicherheit)

Ionut-Gabriel Farcas

PhD thesis, 2020

Multilevel Adaptive Sparse Leja Approximations for Bayesian Inverse Problems.

Ionut-Gabriel Farcas, Jonas Latz, Elisabeth Ullmann, Tobias Neckel, Hans-Joachim Bungartz

SIAM J. Sci. Comput., 2020

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

Ionut-Gabriel Farcas, Tobias Görler, Hans-Joachim Bungartz, Frank Jenko, Tobias Neckel

J. Comput. Phys., 2020

2018

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

Ionut-Gabriel Farcas, Benjamin Uekermann, Tobias Neckel, Hans-Joachim Bungartz

SIAM J. Sci. Comput., 2018

2014

Comparison of Numerical Methods in Uncertainty Quantification.

Ionut-Gabriel Farcas

Proceedings of the 44. Jahrestagung der Gesellschaft für Informatik, Big Data, 2014

E-health decision support system for differential diagnosis.

Roxana Cucu, Camelia Avram, Adina Astilean, Ionut-Gabriel Farcas, José Machado

Proceedings of the IEEE International Conference on Automation, 2014