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Bayesian Analysis and Optimal Experimental Design

Theory-Algorithms

  • T. Sapsis, A. Blanchard, Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modeling with Gaussian process regression, Submitted, (2021) (13 pages). [pdf]
  • A. Blanchard, T. Sapsis, Output-weighted optimal sampling for Bayesian experimental design and rare-event quantification, SIAM/ASA Journal of Uncertainty Quantification, 9 (2021) 564-592. [code] [pdf]
  • S. Rudy, T. Sapsis, Sparse methods for automatic relevance determination, Physica D, 418 (2021) 132843 (16 pages). [code] [pdf]
  • A. Blanchard, T. Sapsis, Bayesian optimization with output-weighted optimal sampling, Journal of Computational Physics, 425 (2021) 109901 (16 pages). [code] [pdf]
  • T. Sapsis, Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples, Proceedings of the Royal Society A, 476 (2020) 20190834 (24 pages). [pdf]
  • M. Mohamad, T. Sapsis, A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems, Proceedings of the National Academy of Sciences, 115 (2018) 11138-11143. [open access link] [supporting information]

Applications

  • A. Blanchard, T. Sapsis, Informative path planning for anomaly detection in environment exploration and monitoring, Submitted, (2021) (24 pages). [pdf]
  • Y. Yang, A. Blanchard, T. Sapsis, P. Perdikaris, Output-weighted sampling for multi-armed bandits with extreme payoffs, Submitted, (2021) (12 pages). [code] [pdf]
  • A. Blanchard, G. C. Maceda, D. Fan, Y. Li, Y. Zhou, B. Noack, T. Sapsis, Bayesian optimization for active flow control, Acta Mechanica Sinica, (2021) In Press (13 pages). [pdf]