11. SciFM24 Animashree Anandkumar: Neural Operators: AI Accelerating Scientific Understanding
Автор: MICDE University of Michigan
Загружено: 2024-04-23
Просмотров: 898
While language models have impressive capabilities of text understanding, they lack the physical understanding and grounding needed in scientific domains. For instance, language models could suggest new hypotheses, such as new molecules or designs, but they lack physical validity and the ability to simulate the processes internally. Hence, the proposed hypotheses still require physical experimentation for validation, which is the biggest bottleneck of scientific research. Numerical simulations offer an alternative to physical experiments, but traditional methods are too slow and infeasible for complex processes observed in many scientific domains. We propose AI-based simulation methods that are 4-5 orders of magnitude faster and cheaper than traditional simulations. They are based on Neural Operators that learn mappings between function spaces and have been successfully applied to weather forecasting, fluid dynamics, carbon capture and storage modeling, and optimized design of medical devices, yielding significant speedups and improvements.
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