The join button will be shown 30 minutes before the seminar starts.
The integration of AI into computational fluid dynamics (CFD) represents a transformative frontier for engineering, yet realizing this potential requires navigating the complexities inherent to fluid mechanics. Bridging the methodological gap between deep learning and traditional CFD simulation, this talk presents work (outlined in the recent preprint: Fluids Intelligence: A forward look on AI foundation models in computational fluid dynamics) to produce a novel scaling law tailored specifically for a fluids foundation model. We explore the theoretical and practical opportunities, analyzing the critical inflection points where model training compute begins to eclipse the high costs of traditional data generation. We conclude by discussing the technical challenges and opportunities the fluids and machine learning communities must collaboratively address to operationalize autonomous computational engineering.