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Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models

https://doi.org/10.1080/00401706.2023.2246157

Sürer, Özge; Plumlee, Matthew; Wild, Stefan M. (April 2024, Technometrics)

Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. The advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.
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Bayesian calibration of viscous anisotropic hydrodynamic simulations of heavy-ion collisions

https://doi.org/10.1103/PhysRevC.108.054905

Liyanage, Dananjaya; Sürer, Özge; Plumlee, Matthew; Wild, Stefan M; Heinz, Ulrich (November 2023, Physical Review C)

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Constructing a Simulation Surrogate with Partially Observed Output

https://doi.org/10.1080/00401706.2023.2210170

Chan, Moses Y.-H.; Plumlee, Matthew; Wild, Stefan M. (June 2023, Technometrics)

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ParMOO: A Python library for parallel multiobjectivesimulation optimization

https://doi.org/10.21105/joss.04468

Chang, Tyler H.; Wild, Stefan M. (February 2023, Journal of Open Source Software)

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Machine-learning-based inversion of nuclear responses

https://doi.org/10.1103/PhysRevC.103.035502

Raghavan, Krishnan; Balaprakash, Prasanna; Lovato, Alessandro; Rocco, Noemi; Wild, Stefan M. (March 2021, Physical Review C)

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Emerging Frameworks for Advancing Scientific Workflows Research, Development, and Education

https://doi.org/10.1109/WORKS54523.2021.00015

Casanova, Henri; Deelman, Ewa; Gesing, Sandra; Hildreth, Michael; Hudson, Stephen; Koch, William; Larson, Jeffrey; McDowell, Mary Ann; Meyers, Natalie; Navarro, John-Luke; et al (November 2021, 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS))

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