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Minisymposium Presentation
Disruption in Science and Engineering Happens at Scale
Presenter
Johannes Brandstetter did his PhD studying Higgs boson decays at the CMS experiment at the Large Hadron Collider at CERN. In 2018, he joined Sepp Hochreiter’s group in Linz, Austria. In 2021, he became ELLIS PostDoc at Max Welling’s lab at the University of Amsterdam, before joining the newly founded Microsoft Lab in Amsterdam. During his time at Microsoft Research, Johannes was working on scaling up of AI driven physics surrogates, most notably for weather and climate simulations. In October 2023, Johannes Brandstetter moved back to Austria and started a new group “AI for data-driven simulations” at the Institute of Machine Learning at the Johannes Kepler University (JKU) in Linz. Additionally, in 2024 Johannes was Chief Researcher at NXAI GmbH. In February 2025, and co-founded Emmi AI in February 2025 where he is currently is the Chief Scientist.
Description
In the era of LLM models, one gets notoriously confronted with the question of where we stand with applicability of large-scale deep learning models within scientific or engineering domains. The discussion starts by reiterating on recent triumphs in weather and climate modeling, making connections to computer vision, physics-informed learning and neural operators. Secondly, we discuss challenges and conceptual barriers which need to be overcome for the next wave of disruption in science and engineering. We showcase recent breakthroughs in multi-physics modeling, computational fluid dynamics, and related fields.