Physics-Informed ML boosts PHM performance, review finds · 1 source tracked

A systematic literature review of 212 studies reveals that Physics-Informed Machine Learning (PIML) is increasingly applied to Prognostics and Health Management (PHM) tasks. While PIML models demonstrate improved predictive performance over traditional data-driven methods, particularly for lithium-ion batteries and bearings, evidence for enhanced generalization and causal inference is less robust. The review suggests future research should focus on developing transferable design patterns, comparative benchmarks for integration strategies, and lightweight, robust uncertainty-aware models for real-world deployment. AI

IMPACT Highlights PIML's growing role in industrial asset management and suggests future research directions for more robust and deployable AI solutions.

RANK_REASON The item is a systematic literature review published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• bearings
• Christopher Braun
• lithium-ion batteries
• Machine Learning
• Physics-Informed Machine Learning

• paper
• other

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