Paris Perdikaris
2026 National Award Finalist — Faculty
Current Position:
Associate Professor
Institution:
University of Pennsylvania
Discipline:
Applied Mathematics
Recognized for: Creating physics-aware artificial intelligence, including Aurora, a global weather forecasting model that runs thousands of times faster than traditional supercomputer simulations.
Areas of Research Interest and Expertise:
Physics-Informed Machine Learning; Scientific Computing and Computational Science; AI for Science; Uncertainty Quantification
Previous Positions:
Principal Research Manager, Microsoft Research, AI4Science, Amsterdam, Netherlands
Assistant Professor, University of Pennsylvania
Postdoctoral Research Scientist, Massachusetts Institute of Technology (Advisor: M. Triantafyllou)
PhD Researcher, Brown University (Advisor: G. Karniadakis)
Research Summary:
Predicting how complex physical systems behave, such as weather or air flowing over a wing, normally takes powerful supercomputers and hours of calculation. Advances by Paris Perdikaris, PhD, using artificial intelligence with the laws of physics built into it, are making these predictions faster and more reliable. Perdikaris’s early work on these “physics-informed” neural networks gave rise to a new field of research, and a related AI weather model, Aurora, now forecasts global weather thousands of times faster than standard methods. These powerful new tools are rapidly speeding up work in climate, clean energy, and medicine.
“I am honored to be recognized for teaching AI to speak the language of nature. By grounding machine learning in physical laws, we can build AI capable of simulating anything in the natural world—from forecasting storms to discoveries not yet imagined.”
Key Publications:
- C. Bodnar, W.P. Bruinsma, A. Lucic, M. Stanley, et al., P. Perdikaris. A Foundation Model for the Earth System. Nature, 2025.
- M. Raissi, P. Perdikaris, G.E. Karniadakis. Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations. Journal of Computational Physics, 2019.
- S. Wang, H. Wang, P. Perdikaris. Learning the Solution Operator of Parametric Partial Differential Equations with Physics-Informed DeepONets. Science Advances, 2021.
- G.E. Karniadakis, I.G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, L. Yang. Physics-Informed Machine Learning. Nature Reviews Physics, 2021.
Other Honors:
2025 JPMorganChase Faculty Research Award, JPMorgan Chase
2023 Samsung GRO Fellow, Samsung Global Research Outreach Program
2021 Scialog Fellow, Research Corporation for Science Advancement
2021 SIAG/CSE Early Career Prize, Society for Industrial and Applied Mathematics
2020 Ford Motor Company Award for Faculty Advising, University of Pennsylvania
2019 AFOSR Young Investigator Award, Air Force Office of Scientific Research
2018 DOE Early Career Award, U.S. Department of Energy
2017 Doctoral Thesis Award, Circle of Hellenic Academics in Boston
2017 Best PhD Thesis Award in Biomedical Engineering, International Conference on Computational and Mathematical Biomedical Engineering
In the Media:
- The New York Times – A.I. Is Poised to Revolutionize Weather Forecasting.
- Wall Street Journal – AI Is Learning to Predict the Weather
- Nature News – Superfast Microsoft AI is first to predict air pollution for the whole world
- HPC Wire – Microsoft’s Aurora AI Foundation Model Goes Beyond Weather Forecasting
- SIAM News – 2021 SIAM Prize Spotlight
- Penn Today – Refining data into knowledge, turning knowledge into action