Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
This work addresses the need for rigorous, reproducible evaluation in scientific ML, but the framework is currently limited to two nonlinear systems and a planned competition, making it an incremental step toward standardization.
The paper proposes a Common Task Framework (CTF) for scientific machine learning to address the lack of standardized benchmarks, which leads to weak baselines and inconsistent evaluations. The framework includes curated datasets and metrics, and initial benchmarking on Kuramoto-Sivashinsky and Lorenz systems demonstrates its utility in revealing method strengths and limitations.
Machine learning (ML) is transforming modeling and control in the physical, engineering, and biological sciences. However, rapid development has outpaced the creation of standardized, objective benchmarks - leading to weak baselines, reporting bias, and inconsistent evaluations across methods. This undermines reproducibility, misguides resource allocation, and obscures scientific progress. To address this, we propose a Common Task Framework (CTF) for scientific machine learning. The CTF features a curated set of datasets and task-specific metrics spanning forecasting, state reconstruction, and generalization under realistic constraints, including noise and limited data. Inspired by the success of CTFs in fields like natural language processing and computer vision, our framework provides a structured, rigorous foundation for head-to-head evaluation of diverse algorithms. As a first step, we benchmark methods on two canonical nonlinear systems: Kuramoto-Sivashinsky and Lorenz. These results illustrate the utility of the CTF in revealing method strengths, limitations, and suitability for specific classes of problems and diverse objectives. Next, we are launching a competition around a global real world sea surface temperature dataset with a true holdout dataset to foster community engagement. Our long-term vision is to replace ad hoc comparisons with standardized evaluations on hidden test sets that raise the bar for rigor and reproducibility in scientific ML.