27 citations · 37 across the 5 of their papers we have counts for
6 papers
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…
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…
Defining Foundation Models for Computational Science: A Call for Clarity and Rigor
Youngsoo Choi, Siu Wun Cheung, Youngkyu Kim +9
The widespread success of foundation models in natural language processing and computer vision has inspired researchers to extend the concept to scientific machine learning and com…
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…
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows
Ivan Zanardi, Simone Venturi, Marco Panesi
This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting flow simulations while ensuring compliance…