Surrogate Modeling Approach to Support Real-Time Structural Assessment and Decision Making
By L. Mainini, Karen E. Willcox. 2015
47 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/8357ab62abb31321befcd7f9d18329590af02e55
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
In Statistical and Nonlinear Physics
- Benjamin Peherstorfer
- Karen E. Willcox
- Renee C. Swischuk
- L. Mainini
- Steven L. Brunton
- Charbel Farhat
- Saifon Chaturantabut
- Max D. Gunzburger
In Statistics and Probability
- Benjamin Peherstorfer
- Karen E. Willcox
- Renee C. Swischuk
- L. Mainini
- Steven L. Brunton
- Max D. Gunzburger
- Charbel Farhat
- Danny C. Sorensen
Citations in the corpus
- Projection-based model reduction: Formulations for physics-based machine learning (2019, methodology): “The work in [18] learns the map from inputs to POD coefficients using an adaptive combination of self-organizing maps and 65”
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems (2015, unknown): “There are some cases, such as onboard structural assessment of an aircraft wing to support real-time mission replanning [4, 166], where one is willing to tolerate an expensive offline phase requiring high-performance computing in order to have the capability to react to aircraft data acquired in rea”