AccScience Publishing / IJAMD / Online First / DOI: 10.36922/IJAMD025440043
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PERSPECTIVE ARTICLE

Physics-informed machine learning for material characterization: A perspective on data-efficient discovery through physics-informed neural networks

Hyeonbin Moon1 ,  Junhyeong Lee1 ,  Jecheon Yu1 ,  Seunghwa Ryu1*
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1 Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
IJAMD, 025440043 https://doi.org/10.36922/IJAMD025440043
Received: 31 October 2025 | Revised: 27 November 2025 | Accepted: 3 December 2025 | Published online: 12 December 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Accurate characterization of material properties is critical for modeling and optimizing advanced systems, yet conventional experimental and simulation-based approaches remain costly and data-intensive. As artificial intelligence evolves from data-driven modeling to physics-informed and knowledge-guided paradigms, this perspective article highlights the role of physics-informed machine learning (PIML), specifically physics-informed neural networks (PINNs), as a key enabler of data-efficient, physically consistent inference. PINNs embed governing equations into the learning process and have demonstrated strong capability in recovering constitutive and transport parameters from sparse or noisy data while preserving physical fidelity. This paper examines the fundamental structure, workflow integration, and recent advances of PINNs in the context of inverse material characterization. It also discusses open challenges in computational cost, training stability, and uncertainty quantification. Looking forward, integration with digital twins, generative modeling, and autonomous experimentation presents a pathway toward interpretable, adaptive, and automated characterization for next-generation intelligent manufacturing.

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Keywords
Physics-informed neural network
Deep learning
Material property characterization
Physical AI
Funding
This work was supported by the InnoCORE program (N10250154) and the National Research Foundation of Korea (RS-2023-00247245) grant of the Ministry of Science and ICT.
Conflict of interest
Seunghwa Ryu serves as an Editorial Board Member of this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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