Gaussian-Process-Driven Optimal Autonomous Data Acquisition for Large-Scale Experimental Facilities

The execution and analysis of ever more complex experiments are increasingly challenged by the vast dimensionality of the parameter spaces that underlie investigations in the biological, chemical, physical, and materials sciences. While an increase in data-acquisition rates should allow broader querying of the parameter space, the complexity of experiments and the subtle dependence of the model function on input parameters remains daunting due to the sheer number of variables. To meet these challenges, new strategies for autonomous data acquisition are rapidly coming to fruition, and are being deployed across a spectrum of scientific experiments. One promising direction that is being explored is the use of Gaussian process regression (GPR). GPR is a quick, non-parametric, and robust approximation and uncertainty quantification method that can directly be applied to autonomous data acquisition. In this talk, I want to present our work on GPR-driven autonomous experimentation at large-scale experimental facilities around the globe. One focus of our work is increased flexibility and domain-awareness of Gaussian processes and the associated mathematical and computational challenges.