Physics Informed Machine Learning (PIML) is an emerging field that combines data-driven machine learning techniques with mathematical physics models. It aims to integrate prior knowledge of physical laws into the learning process, allowing for more accurate and efficient predictions. In this summary, we will explore the key concepts and applications of PIML as mentioned in the top search results.

One of the highly ranked articles on the topic is from Nature titled “Physics-informed machine learning” [1]. The article highlights the seamless integration of data and mathematical physics models in PIML, even in scenarios where the understanding of the underlying physics is limited or uncertain. The authors emphasize the importance of incorporating physical principles into machine learning algorithms to improve their reliability and interpretability.

Another informative source is an explainer article from Pacific Northwest National Laboratory (PNNL) titled “Physics-informed Machine Learning” [3]. It focuses on how PIML leverages prior knowledge of physics to enhance the training of neural networks, leading to more efficient and accurate predictions. The article also mentions the potential applications of PIML in various scientific disciplines, such as materials science and fluid dynamics.

In a survey titled “Physics-Informed Machine Learning” [5], the authors present an overview of the PIML paradigm. They describe PIML as a methodology that builds models leveraging prior knowledge of physics to improve learning tasks. The survey discusses various techniques used in PIML, such as physics-informed neural networks (PINNs) and physics-constrained deep learning.

Wikipedia also provides valuable insights into the topic with its article on “Physics-informed neural networks (PINNs)” [6]. PINNs are a type of universal function approximators that can embed the knowledge of physical laws governing a system. This enables the neural network to learn and predict the behavior of complex physical systems, even with limited or noisy data.

The final search result is a scientific article titled “Physics-informed machine learning for reliability and safety of complex systems” [7]. This article explores the application of physics-informed machine learning strategies for surrogate modeling, particularly in the context of reliability and safety analysis. It discusses how PIML can improve the accuracy and efficiency of predictions for complex systems, ensuring their reliability and safety.

Overall, the search results indicate that Physics Informed Machine Learning is an interdisciplinary field that combines machine learning and mathematical physics models. Its primary goal is to integrate prior knowledge of physical laws into the learning process to improve the accuracy, efficiency, and interpretability of predictions. PIML techniques, such as physics-informed neural networks and physics-constrained deep learning, have been applied in various scientific domains, including materials science, fluid dynamics, and system reliability analysis. By leveraging prior knowledge of physics, PIML aims to bridge the gap between data-driven machine learning and domain-specific physical models, making it a promising approach for solving complex real-world problems.

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