7 citations · 12 across the 7 of their papers we have counts for
8 papers
Integration of local and global surrogates for failure probability estimation
Audrey Gaymann, Juan M. Cardenas, Sung Min Jo +2
This paper presents the development of an algorithm, termed the Global-Local Hybrid Surrogate (GLHS), designed to efficiently compute the probability of rare failure events in comp…
Model Error Embedding with Orthogonal Gaussian Processes
Mridula Kuppa, Khachik Sargsyan, Marco Panesi +1
Computational models of complex physical systems often rely on simplifying assumptions which inevitably introduce model error, with consequent predictive errors. Given data on mode…
MENO: Hybrid Matrix Exponential-based Neural Operator for Stiff ODEs. Application to Thermochemical Kinetics
Ivan Zanardi, Simone Venturi, Marco Panesi
We introduce MENO (''Matrix Exponential-based Neural Operator''), a hybrid surrogate modeling framework for efficiently solving stiff systems of ordinary differential equations (OD…
Physics-Based Machine Learning Closures and Wall Models for Hypersonic Transition-Continuum Boundary Layer Predictions
Ashish S. Nair, Narendra Singh, Marco Panesi +2
Modeling rarefied hypersonic flows remains a fundamental challenge due to the breakdown of classical continuum assumptions in the transition-continuum regime, where the Knudsen num…
Petrov-Galerkin model reduction for collisional-radiative argon plasma
Ivan Zanardi, Alessandro Meini, Alberto Padovan +2
High-fidelity simulation of nonequilibrium plasmas -- crucial to applications in electric propulsion, hypersonic re-entry, and astrophysical flows -- requires state-specific collis…
Scalable nonlinear manifold reduced order model for dynamical systems
Ivan Zanardi, Alejandro N. Diaz, Seung Whan Chung +2
The domain decomposition (DD) nonlinear-manifold reduced-order model (NM-ROM) represents a computationally efficient method for integrating underlying physics principles into a neu…
Petrov-Galerkin model reduction for thermochemical nonequilibrium gas mixtures
Ivan Zanardi, Alberto Padovan, Daniel J. Bodony +1
State-specific thermochemical collisional models are crucial to accurately describe the physics of systems involving nonequilibrium plasmas, but they are also computationally expen…
Stochastic Operator Learning for Chemistry in Non-Equilibrium Flows
Mridula Kuppa, Roger Ghanem, Marco Panesi
This work presents a novel framework for physically consistent model error characterization and operator learning for reduced-order models of non-equilibrium chemical kinetics. By…