Machine learning and AI are now used broadly in the engineering and physical sciences, with a tremendous diversity of architectures and algorithms being developed across disciplines and applications. The AI Institute in Dynamic Systems aims to annually host practitioners in the field, domain experts and ML/AI developers, to help build a common task framework (CTF) for evaluating algorithms. An overarching goal is to develop a taxonomy of enabling architectures and algorithms for the various tasks required in applications, including estimation, forecasting, sensing and control. This is in keeping with the AI Institute’s mission to build and support sustainable challenge data sets for evaluating algorithms and methods for solving modern problems in science and engineering. Join us for this exciting opportunity to bring together a broad range of researchers at the interface of data-driven modeling of systems in engineering and science.
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