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Essential Review Papers on Physics-Informed Neural Networks: A Curated Guide for Practitioners
- Review papers serve as valuable tools to distill essential insights and highlight important trends in Physics-Informed Neural Networks (PINNs).
- The curated guide includes must-read review papers on PINNs covering algorithmic developments, implementation best practices, and real-world applications.
- The collection provides a practical perspective often missing from academic reviews, based on analyzing around 200 arXiv papers on PINNs across various engineering domains.
- Each review paper is explained for its unique perspective and practical takeaways, aiding practitioners in deploying these techniques for real-world challenges.
- Themes in the review papers include fundamental components, theoretical learning process, applications in engineering, available toolsets, emerging trends, and future directions of PINNs.
- The review papers emphasize enhancements in network design, optimization strategies, uncertainty quantification, and theoretical insights, along with showcasing key applications across domains.
- A practical perspective is highlighted in a review paper discussing how PINNs are used to tackle various engineering tasks and presenting distilled recurring functional usage patterns.
- The emphasis on solving engineering tasks by PINNs provides specific guidance for practitioners, enabling them to leverage established use cases and adapt proven solutions.
- Practitioners seeking insights on training PINNs can benefit from a detailed set of best practices for addressing challenges like spectral bias, unbalanced loss terms, and causality violations.
- Several review papers focus on specific scientific and engineering domains, such as heat transfer, power systems, fluid mechanics, and metal additive manufacturing, offering deeper insights into applications and best practices.
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