Data-driven operator inference for nonintrusive projection-based model reduction

By Benjamin Peherstorfer, Karen E. Willcox. 2016

512 citations recorded by the source corpus.

Subfields: Numerical Analysis, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/807c13e9f72bc067cbabee3c948b2e380aa2dbae

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Numerical Analysis

1. Steven L. Brunton
2. Ricardo Vinuesa
3. Benjamin Peherstorfer
4. Boris Kramer
5. Elizabeth Qian
6. L. Mainini
7. Renee C. Swischuk
8. Karen E. Willcox

In Statistical and Nonlinear Physics

1. Benjamin Peherstorfer
2. Steven L. Brunton
3. Karen E. Willcox
4. Ricardo Vinuesa
5. Renee C. Swischuk
6. L. Mainini
7. Boris Kramer
8. Elizabeth Qian

In Statistics and Probability

1. Steven L. Brunton
2. Benjamin Peherstorfer
3. Ricardo Vinuesa
4. Karen E. Willcox
5. Renee C. Swischuk
6. L. Mainini
7. Boris Kramer
8. Elizabeth Qian

Citations in the corpus

• Learning the solution operator of parametric partial differential equations with physics-informed DeepONets (2021, unknown): “This challenge has motivated a growing literature on reduced-order methods [3, 4, 5, 6, 7, 8, 9] that leverage existing data-sets to build fast emulators, often at the price of reduced accuracy, stability, and generalization performance [10, 11].”
• Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, unknown): “[297] B.”
• Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems (2019, methodology): “The 170 work in [16] offers a data collection strategy for avoiding ill-conditioning in 171 D̂.”
• On closures for reduced order models—A spectrum of first-principle to machine-learned avenues (2021, unknown): “83 First POD model: dynamics of coherent structures and global eddy viscosity modeling 1994 Rempfer and Fasel 99 Linear modal eddy viscosity closure 1995 Eversonand Sirovich 100 Gappy POD 2000 Ravindran 101 Galerkin ROM for optimal flow control problems 2001 Kunisch and Volkwein 102 First numerical a”
• Enhancing computational fluid dynamics with machine learning (2021, methodology): “The operator-inference approach is closely related to Galerkin projection, where a neighboring operator is learned from data [115–117].”
• Projection-based model reduction: Formulations for physics-based machine learning (2019, methodology): “The work in [42] uses least-squares regression to find operators in a similar way to dynamic mode decomposition but is applicable to models with low-order polynomial terms.”