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ARIA Opportunity Space: Scoping Our Planet
AI for Multiscale Physics
We are developing an AI system that can discover clear, understandable equations describing how turbulent flows behave across different scales - a long-standing challenge in physics that affects everything from weather prediction to aerospace design. Our approach combines machine learning with symbolic mathematics to automatically find equations that scientists can interpret and trust, rather than producing black-box predictions. Using data from both laboratory fluid dynamics experiments and detailed computer simulations, we train our AI to identify mathematical patterns that capture turbulent behavior. Success would transform how we model complex fluid systems, enabling more accurate climate predictions and improved industrial designs while demonstrating how AI can be made both powerful and transparent.
Miles Cranmer's Lab
University of Cambridge
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Cambridge
Cranmer Lab
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Miles Cranmer
Assistant Professor of Data Intensive Science at University of Cambridge
Miles Cranmer is a faculty member at the University of Cambridge in the Department of Applied Mathematics and Theoretical Physics, the Institute of Astronomy, and the Kavli Institute for Cosmology. His lab advances AI-driven discovery in the physical sciences, and he leads the machine learning module of Cambridge’s MPhil in Data Intensive Science. Miles co-founded PolymathicAI, an international collaboration building large-scale foundation models for scientific data, and his group develops an ecosystem of open-source tools, such as PySR, which are used widely across the sciences. Before moving his group to Cambridge, he spent time at Princeton University, Google DeepMind, and Flatiron Institute.
