We are working on developing computational methods that best combine principles of physics and thermodynamics with data obtained from micromechanical simulations as well as experiments. Our objective is to create accurate and explainable predictive models. These fall under three main categories, distance-minimizing data-driven computational mechanics, thermodynamics-informed neural networks and graph neural networks.
