# Physics-Informed Machine Learning in Prognostics and Health Management: A Systematic Literature Review

Published: 2026-08-12 · Updated: 2026-08-12 · Category: Research

Source page: https://www.sipply.dev/story/physics-informed-machine-learning-in-prognostics-and-health-0a9a6020 · 中文: https://www.sipply.dev/zh/story/physics-informed-machine-learning-in-prognostics-and-health-0a9a6020

## Summary

A recent systematic literature review has examined 212 studies focused on the application of Physics-Informed Machine Learning (PIML) in the field of Prognostics and Health Management (PHM). The review highlights the promising potential of PIML to improve predictive performance in various health management scenarios. However, it also uncovers notable limitations, particularly regarding the generalizability of the models and the necessity for additional research into transferable design patterns and uncertainty-aware approaches. This indicates a critical need for ongoing investigation to fully harness the capabilities of PIML in practical applications.

## Key takeaways

- PIML shows significant promise in enhancing predictive performance in PHM.
- There are limitations related to the generalizability of current PIML models.
- Further research is needed on transferable design patterns and uncertainty-aware models.

## Why it matters

This research is significant as it underscores the advancements in integrating physics-based models with machine learning, which can lead to more accurate health management solutions. Addressing the identified limitations will be crucial for the successful implementation of these technologies in real-world applications.

- **Product managers:** For product managers, understanding the potential and limitations of PIML can guide the development of more effective health management products that leverage advanced predictive analytics.
- **Developers:** Developers and engineers should take note of the need for improved generalizability and uncertainty-aware models, as these areas present opportunities for innovation and enhancement in PIML applications.
- **Students and job seekers:** Students and job-seekers in the AI and health management fields should recognize the growing importance of PIML, as expertise in this area could lead to valuable career opportunities in emerging technologies.

## Sources

- [Physics-Informed Machine Learning in Prognostics and Health Management: A Systematic Literature Review](https://arxiv.org/abs/2608.10047) (arXiv cs.LG, 2026-08-12)

## 中文

### 物理启发的机器学习在预测与健康管理中的应用：系统文献综述

最近的一项系统文献综述考察了212项研究，聚焦于物理启发的机器学习（PIML）在预测与健康管理（PHM）领域的应用。综述强调了PIML在改善各种健康管理场景中的预测性能的潜力，但也揭示了显著的局限性，特别是在模型的可推广性方面，以及对可转移设计模式和不确定性意识方法的进一步研究的必要性。这表明需要持续的研究，以充分利用PIML在实际应用中的能力。

**要点：**

- PIML在提升PHM中的预测性能方面显示出显著潜力。
- 当前PIML模型的可推广性存在局限性。
- 需要进一步研究可转移设计模式和不确定性意识模型。

**为什么重要：** 这项研究的重要性在于强调了将基于物理的模型与机器学习相结合的进展，这可以导致更准确的健康管理解决方案。解决识别出的局限性对于这些技术在实际应用中的成功实施至关重要。

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