12 citations · 32 across the 7 of their papers we have counts for
7 papers
Block-structured Operator Inference for coupled multiphysics model reduction
Benjamin G. Zastrow, Anirban Chaudhuri, Karen E. Willcox +2
This paper presents a block-structured formulation of Operator Inference as a way to learn structured reduced-order models for multiphysics systems. The approach specifies the gove…
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…
Non-intrusive reduced-order modeling for dynamical systems with spatially localized features
Leonidas Gkimisis, Nicole Aretz, Marco Tezzele +3
This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed…
Formal Verification of Digital Twins with TLA and Information Leakage Control
Luwen Huang, Lav R. Varshney, Karen E. Willcox
Verifying the correctness of a digital twin provides a formal guarantee that the digital twin operates as intended. Digital twin verification is challenging due to the presence of…
Adaptive planning for risk-aware predictive digital twins
Marco Tezzele, Steven Carr, Ufuk Topcu +1
This work proposes a mathematical framework to increase the robustness to rare events of digital twins modelled with graphical models. We incorporate probabilistic model-checking a…
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…
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…