5 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…
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…
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…
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…