Algorithm Development for Black-Box Global Optimization
Optimization problem characteristicsGoal: find a near-optimal solution by doing only very few expensive simulation model evaluations in order to keep the optimization time acceptableChallengeMore sophisticated optimization algorithms are needed to efficiently and effectively tune the simulation model parametersApproachWe use a computationally cheap approximation s(x) (the surrogate model) of the expensive function f(x) in order to predict function values at unsampled points and guide the search for improved solutions:
A general surrogate model optimization algorithm works as follows:
Selected applicationsCombustion simulations: more to come.. Cosmology: more to come... Event generator tuning: more to come... Co-optimization of fuels and engines: more to come... Ongoing workContactFor more information, contact Juliane MuellerRelated publicationsT. Takhtaganov, Z. Lukic, J. Mueller, D. Morozov, "Cosmic Inference: Constraining Parameters with Observations and a Highly Limited Number of Simulations", The Astrophysical Journal., 2021. [link]. J. Mueller, J. Park, R. Sahu, C. Varadharajan, B. Arora, B. Faybishenko, D. Agarwal, "Surrogate optimization of deep neural networks for groundwater predictions", Journal of Global Optimization., 2020. [link]. J. Mueller, "An algorithmic framework for the optimization of computationally expensive bi-fidelity black-box problems", INFOR: Information Systems and Operational Research., 2019. [link]. W. Langhans, J. Mueller, and W. Collins, "Optimization of the Eddy-Diffusivity/Mass-Flux shallow cumulus and boundary-layer parameterization using surrogate models", Journal of Advances in Modeling Earth Systems., 2019. [link]. J. Mueller and M. Day, "Surrogate optimization of computationally expensive black-box problems with hidden constraints", INFORMS Journal on Computing., 2019. [link]. T. Takhtaganov, J. Mueller "Adaptive Gaussian process surrogates for Bayesian inference", preprint, 2018 [link]. O. Karslioglu, M. Gehlmann, J. Mueller, S. Nemsak, J. Sethian, A. Kaduwela, H. Bluhm, C.S. Fadley "An Efficient Algorithm for Automatic Structure Optimization in X-ray Standing-Wave Experiments", Journal of Electron Spectroscopy and Related Phenomena, 2018 [link]. G. Conti, S Nemsak, C.-T. Kuo, M. Gehlmann, C. Conlon, A. Keqi, A. Rattanachata, O. Karslioglu, J. Mueller, J. Sethian, H. Bluhm, J.E. Rault, J.P. Rueff, H. Fang, A. Javey, C.S. Fadley "Characterization of free standing InAs quantum membranes by standing wave hard x-ray photoemission spectroscopy", APL Materials, 2018 [link]. J. Mueller, J. Woodbury "GOSAC: Global Optimization with Surrogate Approximation of Constraints", Journal of Global Optimization, 2017 [link]. J. Mueller "SOCEMO: Surrogate Optimization of Computationally Expensive Multi-Objective Problems", INFORMS Journal on Computing, 2017 [link]. J. Mueller, "MISO: Mixed-Integer Surrogate Optimization Framework", Optimization and Engineering, to appear, 2015 [link]. J. Mueller, R. Paudel, N. Mahowald, C. Shoemaker, "CH4 Parameter Estimation in CLM4.5bgc Using Surrogate Global Optimization", Geoscientific Model Development, 8:141-207, 2015 [pdf]. |