Machine Learning at the Flatiron: Johannes Brandstetter

Title: Closing the Gap Between Scientific Foundation Models and Real-World Applications

Abstract: In the era of LLM models, one gets notoriously confronted with the question of where we stand with the applicability of large-scale deep learning models within scientific or engineering domains. The talk is motivated by recent triumphs in weather and climate modeling, and discusses potentials, breakthroughs, and remaining challenges in fluid dynamics and related engineering fields. Concretely, we showcase recent work in scaling neural networks to model multi-physics phenomena and computational fluid dynamics as used in automotive engineering. Finally, we outline challenges and potential solutions when it comes to scalability beyond traditional numerical schemes and discuss the respective impact on industry and scientific environments.