Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with advanced machine learning (ML) techniques. Application-centric objective areas for which these approaches have been applied are summarized, and then classes of methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks are described.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 This article provides a structured overview of such techniques.
• 2 Application-centric objective areas for which these approaches have been applied are summarized, and then classes of methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks are described.
• 3 We then provide a taxonomy of these existing techniques, which uncovers knowledge gaps and potential crossovers of methods between disciplines that can serve as ideas for future research.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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