Beyond Hope: Kienholz and the Inland Northwest Exhibition

Master of Fine Arts Thesis Exhibition

Subversive Intent: Selections from the Collection Exhibition

Attend free fitness classes during finals week to help bust stress! Exercise (and fun!) are proven to aid concentration and memory.

Aqueous Zinc Metal Batteries (AZMBs) offer significant promise as an alternative to conventional Lithium-ion batteries due to their high capacity, cost-effectiveness, and safety. However, practical implementation faces challenges such as dendrite formation, hydrogen evolution reaction (HER), and passivation of the anode.

Looking for ways to get connected?

Come join us for a fun-filled afternoon getting to know your executive board and fellow Global students.

You can win awesome prizes in…

Dr. Maiorov, from Los Alamos National Laboratory, is visiting WSU Chemistry and Math department to discuss, How to Learn Physics Using ‘Tuning Forks’.

This talk will present a different programming perspective for physics-informed machine learning (PIML) of dynamical system models, learning to optimize, and learning to control methods. We will discuss the opportunity to develop a unified PIML framework by leveraging the conceptual similarities between these distinct approaches. Specifically, we introduce differentiable predictive control (DPC) as a sampling-based learning to control method that integrates the principles of parametric model predictive control (MPC) with physics-informed neural networks (PINNs). We also show how to use recent developments in control barrier functions and neural Lyapunov functions to obtain online performance guarantees for learning-based control policies. We demonstrate the performance of these PIML methods in a range of simulation case studies, including modeling of networked dynamical systems, robotics, building control, and dynamic economic dispatch problem in power systems.