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京东 11.11 红包
Control Design Based on Deep Learning by Draguna Vrabie
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Control Design Based on Deep Learning by Draguna Vrabie, March 10, 2023 Abstract: Innovation in deep learning methods, tools, and technology offers an unprecedented opportunity to transform the control engineering practice and bring much excitement to control systems theory research. In this talk, I will introduce recent results in modeling dynamic systems with deep learning representations that embed domain knowledge. I will also discuss differentiable predictive control, a data-driven approach that uses physics-informed deep learning representations to synthesize predictive control policies. I’ll illustrate the concepts with examples from engineering applications. Bio: Draguna Vrabie is a Chief Data Scientist in the Data Sciences and Machine Intelligence Group at Pacific Northwest National Laboratory. She serves as Team Lead for the Autonomous Learning and Reasoning Team and as Thrust Lead for PNNL’s Data Model Convergence Initiative. Her work focuses on design and optimization of autonomous systems by bringing together scientific machine learning with theoretical methods for decision and control. Her research bridges theory, computational methods, and practical application to energy and critical infrastructure systems. Vrabie leads PNNL’s contributions the ECP ExaLearn investment on scalable reinforcement learning, and PNNL’s project portfolio on applied ML for modeling, simulation and control for energy efficiency building systems in support of EERE’s Building Technology Office. Vrabie co-authored two books on optimal control, reinforcement learning, and differential games and has published over seventy peer-reviewed journal and conference papers....
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