Optimization-in-the-Loop AI for Energy and Climate - IPAM at UCLA
Explore optimization-in-the-loop AI techniques for addressing climate change and energy challenges in this comprehensive lecture. Delve into the integration of artificial intelligence and machine learning methods with physics-based constraints and complex decision-making processes. Learn how this framework can be applied to design learning-based controllers that enforce stability criteria and operational constraints in low-carbon power grids and energy-efficient buildings. Discover task-based learning procedures that consider downstream decision-making processes, significantly improving performance and preventing critical failures. Gain insights into differentiable optimization, deep reinforcement learning, robust control, and decision-cognizant approaches for demand forecasting and power system optimization. Understand the potential of these innovative techniques in unlocking AI and ML capabilities for high-impact climate action problems.