CD Technical Meeting (ML3): Sensor Qualification and Placement using Probabilistic AI
Machine Learning, Uncertainty Quantification and Data Science
Abstract:
How can we design good sensing systems in fusion? In this talk, we introduce a novel way of qualifying, optimizing, and placing sensors and diagnostics based upon the framework of Bayesian Experimental Design. Accelerated by modern probabilistic AI techniques, this framework is agnostic and can quickly configure integrated systems of heterogeneous sensors. Ultimately, the framework allows scientists, diagnosticians, and engineers to design experiments that reduce the uncertainty of the observed system, and maximize information learnt from the experiment.