Domain-Aware Gaussian Processes and High-Performance Mathematical Optimization for Optimal Autonomous Data Acquisition

ORAL  · Invited

Abstract

Gaussian processes and Gaussian-related stochastic processes have been shown to be powerful tools for stochastic function approximation and autonomous control of data acquisition due to their robustness, analytical tractability, and natural inclusion of uncertainty quantification. In this talk, I want to present our work on a general, flexible, and powerful GP-driven framework for autonomous data acquisition. The focus of this work lies on making Gaussian (related) processes more flexible and domain aware, how the added flexibility and domain-awareness can be used for decision-making, and the computational and mathematical challenges that come with these advancements. In particular, I will focus on the challenge of hyperparameter optimization and how it impacts on-the-fly autonomous decision-making.

*The work was funded through the Center for AdvancedMathematics for Energy Research Applications (CAMERA), which is jointly funded by the Advanced Scientific Computing Research (ASCR) and Basic Energy Sciences (BES) within the Department of Energy's Office of Science, under Contract No. DE-AC02-05CH11231.

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Publication: Marcus M Noack, Petrus H Zwart, Daniela M Ushizima, Masafumi Fukuto, Kevin G Yager,Katherine C Elbert, Christopher B Murray, Aaron Stein, Gregory S Doerk, Esther HRTsai, et al. Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities. Nature Reviews Physics, pages 1–13, 2021.

Marcus M Noack and James A Sethian. Autonomous discovery in science and engineering.Technical report, USDOE Office of Science (SC)(United States), 2021.Marcus M Noack and James A Sethian. Advanced stationary and non-stationary kernel designs for domain-aware gaussian processes. arXiv preprint arXiv:2102.03432, 2021.

Marcus M Noack, Gregory S Doerk, Ruipeng Li, Masafumi Fukuto, and Kevin G Yager. Advances in kriging-based autonomous x-ray scattering experiments.Scientific reports,10(1):1–17, 2020.

Marcus M Noack, Gregory S Doerk, Ruipeng Li, Jason K Streit, Richard A Vaia, Kevin GYager, and Masafumi Fukuto. Autonomous materials discovery driven by gaussian process regression with inhomogeneous measurement noise and anisotropic kernels. Scientific reports, 10(1):1–16, 2020.

Presenters

  • Marcus Noack

    • Lawrence Berkeley National Laboratory
    • Lawrence Berkeley National Lab

Authors

  • Marcus Noack

    • Lawrence Berkeley National Laboratory
    • Lawrence Berkeley National Lab