19 citations · 84 across the 15 of their papers we have counts for
16 papers
Learning Long-Term Stable Operator Inference Reduced-Order Models of Fluid Flows through Online Spatial Filtering
Ian Moore, Ping-Hsuan Tsai, Anthony Gruber +4
This paper introduces an online evolve--filter--relax (EFR) strategy for long-term stability of Operator Inference (OpInf) reduced-order models (ROMs) of complex fluid flow simulat…
Convolution Operator Network for Forward and Inverse Problems (FI-Conv): Application to Plasma Turbulence Simulations
Xingzhuo Chen, Anthony Poole, Ionut-Gabriel Farcas +2
We propose the Convolutional Operator Network for Forward and Inverse Problems (FI-Conv), a framework capable of predicting system evolution and estimating parameters in complex sp…
Machine Learning for Electron-Scale Turbulence Modeling in W7-X
Ionut-Gabriel Farcas, Don Lawrence Carl Agapito Fernando, Alejandro Banon Navarro +2
Constructing reduced models for turbulent transport is essential for accelerating profile predictions and enabling many-query tasks such as parameter exploration and design optimiz…
Fast prediction of plasma instabilities with sparse-grid-accelerated optimized dynamic mode decomposition
Kevin Gill, Ionut-Gabriel Farcas, Silke Glas +1
Parametric data-driven reduced-order models (ROMs) that embed dependencies in a large number of input parameters are crucial for enabling many-query tasks in large-scale problems.…
A parallel implementation of reduced-order modeling of large-scale systems
Ionut-Gabriel Farcas, Rayomand P. Gundevia, Ramakanth Munipalli +1
Motivated by the large-scale nature of modern aerospace engineering simulations, this paper presents a detailed description of distributed Operator Inference (dOpInf), a recently d…
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 +1
High-performance computing (HPC) has revolutionized our ability to perform detailed simulations of complex real-world processes. A prominent contemporary example is from aerospace…
Advanced surrogate model for electron-scale turbulence in tokamak pedestals
Ionut-Gabriel Farcas, Gabriele Merlo, Frank Jenko
We derive an advanced surrogate model for predicting turbulent transport at the edge of tokamaks driven by electron temperature gradient (ETG) modes.Our derivation is based on a re…
Scientific Machine Learning Based Reduced-Order Models for Plasma Turbulence Simulations
Constantin Gahr, Ionut-Gabriel Farcas, Frank Jenko
This paper investigates non-intrusive Scientific Machine Learning (SciML) Reduced-Order Models (ROMs) for plasma turbulence simulations. In particular, we focus on Operator Inferen…
Domain decomposition for data-driven reduced modeling of large-scale systems
Ionut-Gabriel Farcas, Rayomand P. Gundevia, Ramakanth Munipalli +1
This paper focuses on the construction of accurate and predictive data-driven reduced models of large-scale numerical simulations with complex dynamics and sparse training datasets…
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification
Ionut-Gabriel Farcas, Benjamin Peherstorfer, Tobias Neckel +2
Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for variance reduction to make tractable uncertainty quantification even when numerically simulating t…
A general framework for quantifying uncertainty at scale
Ionut-Gabriel Farcas, Gabriele Merlo, Frank Jenko
In many fields of science, comprehensive and realistic computational models are available nowadays. Often, the respective numerical calculations call for the use of powerful superc…
Turbulence suppression by energetic particles: A sensitivity-driven dimension-adaptive sparse grid framework for discharge optimization
Ionut-Gabriel Farcas, Alessandro Di Siena, Frank Jenko
A newly developed sensitivity-driven approach is employed to study the role of energetic particles in suppressing turbulence-inducing micro-instabilities for a set of realistic JET…
Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis
Julia Konrad, Ionut-Gabriel Farcas, Benjamin Peherstorfer +4
The linear micro-instabilities driving turbulent transport in magnetized fusion plasmas (as well as the respective nonlinear saturation mechanisms) are known to be sensitive with r…
Reduced operator inference for nonlinear partial differential equations
Elizabeth Qian, Ionut-Gabriel Farcas, Karen Willcox
We present a new scientific machine learning method that learns from data a computationally inexpensive surrogate model for predicting the evolution of a system governed by a time-…
Multilevel adaptive sparse Leja approximations for Bayesian inverse problems
Ionut-Gabriel Farcas, Jonas Latz, Elisabeth Ullmann +2
Deterministic interpolation and quadrature methods are often unsuitable to address Bayesian inverse problems depending on computationally expensive forward mathematical models. Whi…
Sensitivity-driven adaptive sparse stochastic approximations in plasma microinstability analysis
Ionut-Gabriel Farcas, Tobias Görler, Hans-Joachim Bungartz +2
Quantifying uncertainty in predictive simulations for real-world problems is of paramount importance - and far from trivial, mainly due to the large number of stochastic parameters…