Towards reference models for Engineering Abstract

In the era of LLMs, 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 breakthroughs in multi-physics modeling, computational fluid dynamics, and related fields, putting an emphasis on what it takes to build reference models for whole industry verticals. We relate those breakthroughs to advancements in engineering and much faster process cycles.