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However, the inherent \u2018black-box\u2019 nature of ML presents a major challenge in interpreting the mechanism and outcomes of the models. Moreover, reliable ML predictions are highly dependent on the amount and quality of training data. To address these issues, physics-informed machine learning (PIML), also known as scientific machine learning, has emerged as a new research field. PIML incorporates physical and domain knowledge into ML models to guide the ML training process, which enables more interpretable and reliable models. To fully leverage the advantages of PIML and promote the advancement of design and manufacturing, it is essential for researchers to understand the available PIML methodologies and the technical challenges of PIML methods. This article provides a systematic review of the state-of-the-art in PIML, focusing on the methodologies of integrating physics into ML. The PIML techniques can be grouped into three categories, including hybrid models, physical loss-based models, and physics-embedded architectures. Each of these categories is further stratified according to different integration approaches and ML models. The methods and applications of each technique are summarized. In addition, the technical challenges and potential opportunities of PIML are critically analyzed and discussed, providing a roadmap to narrow the research gaps in PIML.<\/jats:p>","DOI":"10.1115\/1.4070100","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T13:48:43Z","timestamp":1760017723000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":3,"title":["Physics-Informed Machine Learning in Design and Manufacturing: Status and Challenges"],"prefix":"10.1115","volume":"25","author":[{"given":"Longye","family":"Pan","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering, , ,","place":["Hong Kong 999077, China"]}]},{"given":"Guangfa","family":"Li","sequence":"additional","affiliation":[{"name":"State University of New York at Binghamton Department of Mechanical Engineering, , , \u00a0","place":["Binghamton, NY, 13902"]}]},{"given":"Tong","family":"Zhu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering, , ,","place":["Hong Kong, China"]}]},{"given":"Dehao","family":"Liu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/008rmbt77","id-type":"ROR","asserted-by":"publisher"}],"name":"State University of New York at Binghamton Department of Mechanical Engineering, , , \u00a0","place":["Binghamton, NY, 13902"]}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01zkghx44","id-type":"ROR","asserted-by":"publisher"}],"name":"Georgia Institute of Technology Woodruff School of Mechanical Engineering, , , \u00a0","place":["Atlanta, GA, 30332"]}]},{"given":"Yanglong","family":"Lu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00q4vv597","id-type":"ROR","asserted-by":"publisher"}],"name":"The Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering, , ,","place":["Hong Kong, China"]}]}],"member":"33","published-online":{"date-parts":[[2025,11,25]]},"reference":[{"issue":"8","key":"2025112513045779200_CIT0001","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1002\/aic.690400806","article-title":"Modeling Chemical Processes Using Prior Knowledge and Neural Networks","volume":"40","author":"Thompson","year":"1994","journal-title":"AIChE J."},{"key":"2025112513045779200_CIT0002","first-page":"9","article-title":"Development of Knowledge Based Artificial Neural Network Models for Microwave Components","author":"Watson","year":"1998"},{"issue":"5","key":"2025112513045779200_CIT0003","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1109\/72.712178","article-title":"Artificial Neural Networks for Solving Ordinary and Partial Differential Equations","volume":"9","author":"Lagaris","year":"1998","journal-title":"IEEE Trans. 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