The article emphasizes the challenges faced by researchers in keeping up with the rapid growth of Physics-Informed Neural Networks (PINNs). It underlines the immense value of review papers in extracting key insights and trends, thus aiding practitioners in efficiently navigating the flood of new information. The author shares a curated list of essential review papers, including their own comprehensive review based on thorough analysis of 200 arXiv papers across multiple engineering domains. This collection aims to support both novice and experienced users in applying PINNs effectively to real-world challenges.
Review papers act as essential tools, condensing vast amounts of information in fast-paced research fields, particularly for practitioners in areas like PINNs, aiding in trend identification and time management.
My curated list of influential review papers on Physics-Informed Neural Networks (PINNs) aims to guide practitioners in understanding crucial advancements, implementation strategies, and real-world applications effectively.
#physics-informed-neural-networks #review-papers #research-trends #machine-learning #engineering-applications
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