Physics-Informed AI

Our research in physics-informed AI focuses on inverse problems in which important physical properties cannot be measured directly but must be inferred from indirect, noisy, and limited observations. We establish physics-informed machine-learning methods that embed governing equations, such as partial differential equations from mechanics, into deep-learning models to estimate latent heterogeneous physical fields. A major application is the estimation of spatially varying bone stiffness and elastic moduli from deformation data using linear elasticity and physics-informed neural networks. This line of work aims to make AI more reliable for scientific and engineering inference by combining data, domain physics, and uncertainty-aware computational modeling.