activity
20182026
most citedScientific Machine Learning Based Reduced-Order Models for Plasma Turbulence Simulations

13 citations · 43 across the 9 of their papers we have counts for

collaborators

16 papers

math.NA2026

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…

cs.LG2026

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…

physics.plasm-ph2025

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…

physics.comp-ph2025★ 2 cited

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.…

cs.DC2025★ 1 cited

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…

math.NA2024★ 10 cited

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…