iamrp.net-Xn3a-1788878306-l853331297 1788878306 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Artificial Intelligence for Materials Science J. Artif. Intell. Mater. Sci. 3149-8957 2025 4 1 Kenji Mori Yuki Sato Rina Okabe Takashi Ito 2025 10.68159/l853331297 https://iamrp.net/pub/journal/1/article/l853331297 Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L. Physics-informed machine learning. Nat Rev Phys. 2021;3(6):422-40. Lu L, Meng X, Mao Z, Karniadakis GE. Deepxde: A deep learning library for solving differential equations. SIAM Rev. 2021;63(1):208-28. Chen Y, Lu L, Karniadakis GE, Dal Negro L. Physics-informed neural networks for inverse problems in nano-optics and metamaterials. Opt Express. 2020;28(8):11618-33. Haghighat E, Juanes R. Sciann: A keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks. Comput Methods Appl Mech Eng. 2021;373:113552. Zhang E, Dao M, Karniadakis GE, Suresh S. Analyses of internal structures and defects in materials using physics-informed neural networks. Sci Adv. 2022;8(7):eabk0644. Miao ZW, Chen Y. Physics-informed neural networks method in high-dimensional integrable systems. Mod Phys Lett B. 2022;36(01):2150531. Hussain A, Sakhaei AH, Shafiee M. Machine learning-based constitutive modelling for material non-linearity: A review. Mech Adv Mater Struct. 2024:1-9. Zhang Z, Gu GX. Physics-informed deep learning for digital materials. Theor Appl Mech Lett. 2021;11(1):100220. Liu X, Qin J, Zhao K, Featherston CA, Kennedy D, Jing Y, et al. Design optimization of laminated composite structures using artificial neural network and genetic algorithm. Compos Struct. 2023;305:116500. Hamel CM, Long KN, Kramer SL. Calibrating constitutive models with full-field data via physics informed neural networks. Strain. 2023;59(2):e12431. Faroughi SA, Pawar N, Fernandes C, Raissi M, Das S, Kalantari NK, et al. Physics-guided, physics-informed, and physics-encoded neural networks in scientific computing. arXiv preprint arXiv:2211.07377. 2022. Wang S, Teng Y, Perdikaris P. Understanding and mitigating gradient flow pathologies in physics-informed neural networks. SIAM J Sci Comput. 2021;43(5):A3055-81. Lee S, Popovics J. Applications of physics-informed neural networks for property characterization of complex materials. RILEM Tech Lett. 2022;7:178-88. Khatib O, Ren S, Malof J, Padilla WJ. Informed deep learning in metamaterials. In: 2023 International Applied Computational Electromagnetics Society Symposium (ACES); 2023; IEEE. p. 1-2. Yang S, Peng S, Guo J, Wang F. A review on physics-informed machine learning for monitoring metal additive manufacturing process. Adv Manuf. 2024;1(2):1-2. Diao Y, Yang J, Zhang Y, Zhang D, Du Y. Solving multi-material problems in solid mechanics using physics-informed neural networks based on domain decomposition technology. Comput Methods Appl Mech Eng. 2023;413:116120. Jeong I, Cho M, Chung H, Kim DN. Data-driven nonparametric identification of material behavior based on physics-informed neural network with full-field data. Comput Methods Appl Mech Eng. 2024;418:116569. Jeong H, Batuwatta-Gamage C, Bai J, Xie YM, Rathnayaka C, Zhou Y, et al. A complete physics-informed neural network-based framework for structural topology optimization. Comput Methods Appl Mech Eng. 2023;417:116401. Han J, Lee Y. Hierarchical learning to solve partial differential equations using physics-informed neural networks. arXiv preprint arXiv:2112.01254. 2021. Khatib O, Ren S, Malof J, Padilla WJ. Metamaterial design with physics informed neural networks. In: Photonic and Phononic Properties of Engineered Nanostructures XIII; 2023; SPIE. p. PC124310O. Anton D, Wessels H. Physics-informed neural networks for material model calibration from full-field displacement data. arXiv preprint arXiv:2212.07723. 2022. Zhou G, Hu Y, Cao Z, Lee MG, Li D. A physics-constrained neural network for crystal plasticity modelling of fcc materials. Scr Mater. 2024;241:115861. Cheng Z, Wang K, Tanvir AM, Shang W, Luo T, Zhang Y, et al. Bayesian optimization of low-temperature nonthermal plasma jet sintering of nanoinks. ACS Appl Mater Interfaces. 2024;16(35):46897-908. Zhao C, Zhang F, Lou W, Wang X, Yang J. A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics. Phys Fluids. 2024;36(10). Cai S, Mao Z, Wang Z, Yin M, Karniadakis GE. Physics-informed neural networks (pinns) for fluid mechanics: A review. Acta Mech Sin. 2021;37(12):1727-38. Shukla K, Jagtap AD, Karniadakis GE. Parallel physics-informed neural networks via domain decomposition. J Comput Phys. 2021;447:110683. Portela CM, Vidyasagar A, Krödel S, Weissenbach T, Yee DW, Greer JR, et al. Extreme mechanical resilience of self-assembled nanolabyrinthine materials. Proc Natl Acad Sci U S A. 2020;117(11):5686-93. Cai S, Wang Z, Wang S, Perdikaris P, Karniadakis GE. Physics-informed neural networks for heat transfer problems. J Heat Transfer. 2021;143(6):060801. Zhang E, Yin M, Karniadakis GE. Physics-informed neural networks for nonhomogeneous material identification in elasticity imaging. arXiv preprint arXiv:2009.04525. 2020. Haghighat E, Raissi M, Moure A, Gomez H, Juanes R. A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics. Comput Methods Appl Mech Eng. 2021;379:113741.