Deep Learning Patterns Enabling AI for Science

Currently, AI and, in particular, deep learning play a major role in science, from data analytics and simulation surrogates to policy and system decisions. This role is likely to increase as ideas from early adopters spread across all academic fields. One can group the structure of “AI for Science” into a few patterns, where one needs to explore examples of each pattern, possibly leading to Foundation models for each or maybe all of them combined. We suggest examining each pattern and supporting it with high-performance, easy-to-use environments for end-to-end systems, including data engineering. This support would cover parallelism, storage and data movement, security, and the user interface. We discuss the relationship between patterns, Foundation models, and benchmarks.