SAIBench: A Structural Interpretation of AI for Science Through
  Benchmarks
Yatao Li Jianfeng Zhan
Artificial Intelligence for Science (AI4S) is an emerging research field that
utilizes machine learning advancements to tackle complex scientific
computational issues, aiming to enhance computational efficiency and accuracy.
However, the data-driven nature of AI4S lacks the correctness or accuracy
assurances of conventional scientific computing, posing challenges when
deploying AI4S models in real-world applications. To mitigate these, more
comprehensive benchmarking procedures are needed to better understand AI4S
models. This paper introduces a novel benchmarking approach, known as
structural interpretation, which addresses two key requirements: identifying
the trusted operating range in the problem space and tracing errors back to
their computational components. This method partitions both the problem and
metric spaces, facilitating a structural exploration of these spaces. The
practical utility and effectiveness of structural interpretation are
illustrated through its application to three distinct AI4S workloads:
machine-learning force fields (MLFF), jet tagging, and precipitation
nowcasting. The benchmarks effectively model the trusted operating range, trace
errors, and reveal novel perspectives for refining the model, training process,
and data sampling strategy. This work is part of the SAIBench project, an AI4S
benchmarking suite.