Regular biography
Paris Perdikaris is an Associate Professor in the Department of Mechanical Engineering at the University of Pennsylvania. Their research focuses on Mechanical Engineering and Applied Mechanics, with a primary affiliation in Computer and Information Science. Perdikaris holds a faculty position at the university and is based in Amy Gutmann Hall, room 521. Their professional website provides additional details about their work and research interests.
Scholar-generated biography
Paris Perdikaris is a researcher at the University of Pennsylvania, specializing in Machine learning, AI for Science, Computational Science and Engineering, and Uncertainty Quantification. His work focuses on developing deep learning frameworks that integrate physical laws into neural networks to solve complex problems in science and engineering. He has published extensively on physics-informed neural networks (PINNs), which are used to address forward and inverse problems involving nonlinear partial differential equations. His research also explores the application of machine learning in cardiovascular flows, subsurface flow, and cardiac activation mapping. Additionally, he investigates methods for uncertainty quantification and data-efficient multi-fidelity modeling.