Ionut Farcas

Assistant Professor

Virginia Tech

United States

Research Interests

Explore related searches

Professors in United States Professors in Fluid Mechanics Fluid Mechanics professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Ionut Farcas is an Assistant Professor at Virginia Tech, United States. His research primarily focuses on distributed computing, scientific machine learning, and uncertainty quantification, particularly in the context of plasma turbulence and energy systems. He has authored several recent articles on topics such as reduced-order models for plasma simulations and application-driven surrogate modeling methods.

Articles (9)

Year:

2025

Year:

2024

Year:

2024

Year:

2023

Year:

2022

Year:

2022

Year:

2021

Year:

2020

Year:

2020

Collaborators (1)

Karen Willcox

University of Texas at Austin

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Ionut Farcas

Research Interests

Explore related searches

Contact this professor

About

Articles (9)

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

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

Advanced surrogate model for electron-scale turbulence in tokamak pedestals

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

A general framework for quantifying uncertainty at scale

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

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

Sensitivity-driven adaptive sparse stochastic approximations in plasma microinstability analysis

Multilevel Adaptive Sparse Leja Approximations for Bayesian Inverse Problems

Collaborators (1)

Karen Willcox

Make your next move.