Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge

2026-01-07

arXiv.org

AI Summary

This work addresses the challenge of efficient low-dimensional modeling for complex aerospace flowfield data by proposing the first standardized, multi-task, and open-participation benchmarking framework. Through a community-wide challenge encompassing compression, prediction, and perception tasks, the framework enables fair comparison and systematic evaluation of data-driven methods. It employs unified evaluation metrics, a blind-testing protocol, and provides both classical and machine learning baselines to support diverse flowfield datasets and application scenarios. Emphasizing the importance of negative results and methodological limitations, the project constructs a comprehensive performance landscape of current approaches. Findings will be disseminated via a virtual special issue in an AIAA journal and associated conference presentations.

Technology Category

• Machine Learning: Evaluation and Analysis
• Data Mining & Knowledge Management: Data Compression
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

We introduce a community challenge designed to facilitate direct comparisons between data-driven methods for compression, forecasting, and sensing of complex aerospace flows. The challenge is organized into three tracks that target these complementary capabilities: compression (compact representations for large datasets), forecasting (predicting future flow states from a finite history), and sensing (inferring unmeasured flow states from limited measurements). Across these tracks, multiple challenges span diverse flow datasets and use cases, each emphasizing different model requirements. The challenge is open to anyone, and we invite broad participation to build a comprehensive and balanced picture of what works and where current methods fall short. To support fair comparisons, we provide standardized success metrics, evaluation tools, and baseline implementations, with one classical and one machine-learning baseline per challenge. Final assessments use blind tests on withheld data. We explicitly encourage negative results and careful analyses of limitations. Outcomes will be disseminated through an AIAA Journal Virtual Collection and invited presentations at AIAA conferences.

Problem

• data-driven modeling
• fluid flows
• model reduction
• compression
• forecasting

Innovation

• data-driven modeling
• fluid flow compression
• flow forecasting
• flow sensing
• community challenge