Abstract: 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 (potential) breakthroughs in multi-physics modeling, computational fluid dynamics, and plasma physics. We relate those breakthroughs to advancements in engineering and much faster process cycles. 

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