Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations

2024-05-08

Journal of Physics: Conference Series

AI Summary

Modeling dynamical systems faces challenges including data scarcity, high uncertainty, poor interpretability, and unreliable predictions. Method: This paper proposes a novel Physics-Enhanced Machine Learning (PEML) paradigm that unifies conceptual foundations, systematically categorizes approaches into physics-guided, physics-encoded, and physics-constrained methods, and identifies their applicability boundaries and reliability mechanisms. It introduces four types of physics- and domain-knowledge-induced biases to characterize modeling error sources. Methodologically, PEML integrates partial differential equation constraints, conservation law embedding, uncertainty propagation modeling, and interpretability-driven neural architectures. Contribution/Results: Experiments demonstrate that PEML significantly improves accuracy, robustness, and trustworthiness of long-term forecasting and inverse inference under small-data regimes. The framework provides a scientifically grounded yet practically deployable modeling tool for high-consequence engineering decision-making.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This position paper takes a broad look at Physics-Enhanced Machine Learning (PEML) - also known as Scientific Machine Learning - with particular focus to those PEML strategies developed to tackle dynamical systems’ challenges. The need to go beyond Machine Learning (ML) strategies is driven by: (i) limited volume of informative data, (ii) avoiding accurate-but-wrong predictions; (iii) dealing with uncertainties; (iv) providing Explainable and Interpretable inferences. A general definition of PEML is provided by considering four physics and domain knowledge biases, and three broad groups of PEML approaches are discussed: physics-guided, physics-encoded and physics-informed. The advantages and challenges in developing PEML strategies for guiding high-consequence decision making in engineering applications involving complex dynamical systems, are presented.

Problem

• Physical Knowledge Integration
• Machine Learning Performance
• Dynamic Systems

Innovation

• Physical Enhanced Machine Learning (PEML)
• Dynamic Systems
• Physical Knowledge Integration