Page 221 - Read Online
P. 221
Page 24 of 25 Wang et al. J. Mater. Inf. 2026, 6, 16
58. He, G.; Zhang, P.; Yin, Z. Active learning inspired multi-fidelity probabilistic modelling of geomaterial property. Comput. Methods.
Appl. Mech. Eng. 2024, 432, 117373. DOI
59. Wang, L.; Zhu, S.; Wu, B.; Xu, Z.; Luo, C.; Wang, Q. Multi-fidelity physics-informed machine learning framework for fatigue life
prediction of additive manufactured materials. Comput. Methods. Appl. Mech. Eng. 2025, 439, 117924. DOI
60. Sabanza-Gil, V.; Barbano, R.; Pacheco Gutiérrez, D.; et al. Best practices for multi-fidelity Bayesian optimization in materials and
molecular research. Nat. Comput. Sci. 2025, 5, 572-81. DOI PubMed
61. Weisberg, S. Nonlinear regression. In Wiley Series in Probability and Statistics; Wiley, 2005. DOI
62. Bishop CM. Pattern recognition and machine learning. Information Science and Statistics. Springer; 2006. https://books.google.com/boo
ks/about/Pattern_Recognition_and_Machine_Learning.html?id=kTNoQgAACAAJ (accessed 2026-03-26).
63. Yang, B.; Chen, B.; Liu, Y.; Chen, J. Gaussian process fusion method for multi-fidelity data with heterogeneity distribution in aerospace
vehicle flight dynamics. Eng. Appl. Artif. Intell. 2024, 138, 109228. DOI
64. Sella, V.; Pham, J.; Chaudhuri, A.; Willcox, K. E. Projection-based multifidelity linear regression for data-poor applications. In AIAA
SCITECH 2023 Forum; National Harbor, MD & Online; Reston, VA: American Institute of Aeronautics and Astronautics, 2023. DOI
65. Ozbayram, O.; Olivier, A.; Graham-brady, L. Heteroscedastic Gaussian process regression for material structure-property relationship
modeling. Comput. Methods. Appl. Mech. Eng. 2024, 431, 117326. DOI
66. Ibrahim, A.; Ataca, C. Prediction of frequency-dependent optical spectrum for solid materials: a multioutput and multifidelity machine
learning approach. ACS. Appl. Mater. Interfaces. 2024, 16, 41145-56. DOI PubMed
67. Mora, C.; Eweis-labolle, J. T.; Johnson, T.; Gadde, L.; Bostanabad, R. Probabilistic neural data fusion for learning from an arbitrary
number of multi-fidelity data sets. Comput. Methods. Appl. Mech. Eng. 2023, 415, 116207. DOI
68. Zanjani Foumani Z, Shishehbor M, Yousefpour A, Bostanabad R. Multi-fidelity cost-aware Bayesian optimization. Comput. Methods.
Appl. Mech. Eng. 2023, 407, 115937. DOI
69. Toal, D. J. J. Some considerations regarding the use of multi-fidelity Kriging in the construction of surrogate models. Struct. Multidisc.
Optim. 2014, 51, 1223-45. DOI
70. Gratiet, L. L.; Garnier, J. Recursive Co-Kriging model for design of computer experiments with multiple levels of fidelity. Int. J.
Uncertain. Quant. 2014, 4, 365-86. DOI
71. Forrester, A. I.; Sóbester, A.; Keane, A. J. Multi-fidelity optimization via surrogate modelling. Proc. R. Soc. A. 2007, 463, 3251-69. DOI
72. Xiao, M.; Zhang, G.; Breitkopf, P.; Villon, P.; Zhang, W. Extended Co-Kriging interpolation method based on multi-fidelity data. Appl.
Math. Comput. 2018, 323, 120-31. DOI
73. Lu, C. K.; Shafto, P. Conditional deep gaussian processes: multi-fidelity kernel learning. Entropy 2021, 23, 1545. DOI PubMed PMC
74. Cutajar, K.; Pullin, M.; Damianou, A.; Lawrence, N.; González, J. Deep Gaussian processes for multi-fidelity modeling. arXiv 2019,
arXiv:1903.07320. Available online: https://arxiv.org/abs/1903.07320 (accessed 26 March 2026).
75. Raissi, M.; Karniadakis, G. Deep multi-fidelity Gaussian processes. arXiv 2016, arXiv:1604.07484. Available online: https://arxiv.org/ab
s/1604.07484 (accessed 26 March 2026).
76. Song, X.; Lv, L.; Sun, W.; Zhang, J. A radial basis function-based multi-fidelity surrogate model: exploring correlation between
high-fidelity and low-fidelity models. Struct. Multidisc. Optim. 2019, 60, 965-81. DOI
77. Acar, P. Crystal plasticity model calibration for Ti-7Al alloy with a multi-fidelity computational scheme. Integr. Mater. Manuf. Innov.
2018, 7, 186-94. DOI
78. Shah, A. A.; Leung, P. K.; Xing, W. W. Rapid high-fidelity quantum simulations using multi-step nonlinear autoregression and graph
embeddings. npj. Comput. Mater. 2025, 11, 57. DOI
79. Pan, W.; Sun, L.; Guo, C.; et al. An efficient/accurate multi-scale fatigue prediction method for Metal-Polymer hybrid (MPH) interface.
Int. J. Fatigue. 2024, 184, 108304. DOI
80. Saunders, R. N.; Teferra, K.; Elwany, A.; Michopoulos, J. G.; Lagoudas, D. Metal AM process-structure-property relational linkages
using Gaussian process surrogates. Additive. Manuf. 2023, 62, 103398. DOI
81. Morse, L.; Khodaei, Z. S.; Aliabadi, M. A multi-fidelity modelling approach to the statistical inference of the equivalent initial flaw size
distribution for multiple-site damage. Int. J. Fatigue. 2019, 120, 329-41. DOI
82. Tran, A.; Tranchida, J.; Wildey, T.; Thompson, A. P. Multi-fidelity machine-learning with uncertainty quantification and Bayesian
optimization for materials design: application to ternary random alloys. J. Chem. Phys. 2020, 153, 074705. DOI PubMed
83. Menon, N.; Mondal, S.; Basak, A. Multi-fidelity surrogate-based process mapping with uncertainty quantification in laser directed
energy deposition. Materials 2022, 15, 2902. DOI PubMed PMC
84. Wiangkham, A.; Aengchuan, P.; Kasemsri, R.; Pichitkul, A.; Tantrairatn, S.; Ariyarit, A. Improvement of mixed-mode I/II fracture
toughness modeling prediction performance by using a multi-fidelity surrogate model based on fracture criteria. Materials 2022, 15,
8580. DOI PubMed PMC
