Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms

TL;DR

The paper introduces a Common Task Framework (CTF) for scientific machine learning to standardize evaluation, improve reproducibility, and facilitate fair comparison of algorithms across diverse scientific domains.

Contribution

It proposes a structured benchmark with datasets and metrics for evaluating scientific ML algorithms, inspired by successful CTFs in NLP and computer vision.

Findings

Benchmarking on Kuramoto-Sivashinsky and Lorenz systems demonstrates the framework's effectiveness.

The CTF reveals strengths and limitations of different algorithms.

A community challenge on sea surface temperature data is planned.

Abstract

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.