MEAM Seminar: “Digital Twins for the Earth System”
February 4 at 10:15 AM - 11:15 AM
Reliable forecasts of the Earth system are crucial for human progress and safety from natural disasters. Artificial intelligence offers substantial potential to improve prediction accuracy and computational efficiency in this field, however this remains underexplored in many domains. Here we introduce Aurora, a large-scale foundation model for the Earth system trained on over a million hours of diverse data. Aurora outperforms operational forecasts for air quality, ocean waves, tropical cyclone tracks, and high-resolution weather forecasting at orders of magnitude loss computational cost than dedicated existing systems. With the ability to fine-tune Aurora to diverse application domains at only modest computational cost, Aurora represents significant progress in making actionable Earth system predictions accessible to anyone.
Paris Perdikaris
Associate Professor, Department of Mechanical Engineering & Applied Mechanics, University of Pennsylvania
Paris Perdikaris is an Associate Professor of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. He received his Ph.D. in Applied Mathematics from Brown University (2015), and worked as a postdoctoral researcher at the Massachusetts Institute of Technology (2015-2017). His research interests span a range of topics at the interface of computational science and machine learning, including the development of foundation models for Earth system modeling, physics-informed neural networks and neural operators, generative models, and uncertainty quantification for sequential decision making in scientific and engineering applications.