REFERENCES
1. Soni, R.; Verma, R.; Kumar, G. R.; Singh, H. Progress in aerospace materials and ablation resistant coatings: a focused review. Opt. Laser. Technol. 2024, 177, 111160.
2. Guo, M.; Yu, K.; Yang, J.; Zhang, P.; Zhang, Y.; Zhu, D. B2O3-reinforced ablative materials with superior and comprehensive ablation resistance used in aerospace propulsion thermal protection systems. Polym. Degrad. Stab. 2024, 223, 110740.
3. Kumar, A.; Ranjan, C.; Kumar, K.; Reddy, M. H.; Babu, B. S.; Katiyar, J. K. State-of-the-art on advancements in carbon-phenolic and carbon-elastomeric ablatives. Polymers 2024, 16, 1461.
4. Xu, W.; Song, W.; Jia, X.; et al. Nano-silica modified lightweight and high-toughness carbon fiber/phenolic ablator with excellent thermal insulation and ablation performance. Def. Technol. 2024, 31, 192-9.
5. Xia, C.; Xie, W.; Meng, S.; Gao, B.; Ye, J. Preparation of integrated carbon fiber stitched fabric reinforced (SiBCN) ceramic/resin double-layered composites for ablation resistance, thermal insulation and compression resistance performance. Compos. Sci. Technol. 2024, 252, 110629.
6. Vekariya, N.; Patel, B.; Patel, R.; Valand, J. Microstructural investigation and performance of carbon matrix composites developed through facile pitch modification route. Diam. Relat. Mater. 2024, 145, 111056.
7. Agarwal, N.; Rangamani, A.; Bhavsar, K.; et al. An overview of carbon-carbon composite materials and their applications. Front. Mater. 2024, 11, 1374034.
8. Moyer, C. B.; Wool, M. R. Aerotherm charring material thermal response and ablation program, version 3. volume 1. Program description and sample problems. 1970. https://archive.org/details/DTIC_AD0875062. (accessed 11 Apr 2025)
9. Chen, Y. K.; Milos, F. Three-dimensional ablation and thermal response simulation system. In Proceedings of the 38th AIAA thermophysics conference, Toronto, Canada. Jun 06-09, 2005. 2012; pp. 5064.
10. Chen, Y. K.; Milos, F. Multidimensional effects on heatshield thermal response for the Orion crew module. In Proceedings of the 39th AIAA Thermophysics Conference, Miami, USA. Jun 25-28, 2007. 2012; pp. 4397.
11. Chen, Y. K.; Milos, F.; Gokcen, T. Validation of a three-dimensional ablation and thermal response simulation code. In Proceedings of the 10th AIAA/ASME Joint Thermophysics and Heat Transfer Conference, Chicago, USA. Jun 28 - Jul 01, 2010. 2012; pp. 4645.
12. Dec, J. A. Three dimensional finite element ablative thermal response analysis applied to heatshield penetration design. 2010. https://www.proquest.com/openview/6f7df22a64ad48ff6a7b07b10532fcb4/1?cbl=18750&pq-origsite=gscholar. (accessed 11 Apr 2025).
13. Dec, J.; Laub, B.; Braun, R. Two-dimensional finite element ablative thermal response analysis of arcjet stagnation tests. In Proceedings of the 42nd AIAA Thermophysics Conference, Honolulu, USA. Jun 27-30, 2011. 2012; pp. 3617.
14. Kavimani, V.; Gopal, P. M.; Thankachan, T.; Sivamaran, V. 5 - Evolution and recent advancements of composite materials in thermal applications. In: Applications of Composite Materials in Engineering. Elsevier; 2025. pp. 119-38.
15. Meicong, W.; Xin, Y.; Jixiang, S.; et al. Effect of phenolic resin pyrolysis on thermal properties of SiFRP composites under high heating rates. J. Phys. Conf. Ser. 2024, 2891, 112023.
16. Song, J.; Zhang, Y. Effect of an interface layer on thermal conductivity of polymer composites studied by the design of double-layered and triple-layered composites. Int. J. Heat. Mass. Transfer. 2019, 141, 1049-55.
17. Xu, Y.; Ye, H.; Zhang, L.; Cai, Q. Investigation on the effective thermal conductivity of carbonized high silica/phenolic ablative material. Int. J. Heat. Mass. Transfer. 2017, 115, 597-603.
18. Alotaibi, H. Numerical modelling of manufacturing of graphene-modified hybrid composites for structural applications. 2023. https://www.proquest.com/openview/20298841422af4144c4020fb69aa34df/1?cbl=2026366&diss=y&pq-origsite=gscholar. (accessed 11 Apr 2025).
19. Kumar, C. V.; Kandasubramanian, B. Advances in ablative composites of carbon based materials: a review. Ind. Eng. Chem. Res. 2019, 58, 22663-701.
20. Guo, N.; Wang, M.; Wang, J.; Wu, Z.; Gao, J. Utilizing a multi-layer structure to regulate the thermal conductivity of an advanced BN + EP composite insulation material: effects of content, number of layers, and curing temperature on composites. Polym. Compos. 2024, 45, 3536-50.
22. Reis, L. M. M.; da Silveira, P. H. P. M.; Chaves, Y. S.; et al. Thermal and ballistic characterization of epoxy matrix composites reinforced with babassu (Attalea speciosa) fiber: an experimental investigation. J. Mater. Res. Technol. 2025, 35, 2176-87.
23. Chen, L.; Zhao, P.; Xie, H.; Yu, W. Thermal properties of epoxy resin based thermal interfacial materials by filling Ag nanoparticle-decorated graphene nanosheets. Compos. Sci. Technol. 2016, 125, 17-21.
24. Guo, L.; Peng, J.; Guo, C.; Huo, C.; Sun, R.; Zhang, Y. Ablation performance of supersonic atmosphere plasma sprayed tungsten coating under oxyacetylene torch and plasma torch. Vacuum 2017, 143, 262-70.
25. Zha, B. L.; Su, Q. D.; Shi, Y. A.; Wang, J. J.; He, Q. Study on plasma ablation behavior of C/C composite materials under particle erosion. IOP. Conf. Ser. Mater. Sci. Eng. 2018, 423, 012094.
26. Yi, Z.; Ran, L.; Yi, M. Differences in microstructure and properties of C/C composites brazed with Ag-Cu-Ti and Ni-Cr-P-Ti pasty brazing filler. Vacuum 2019, 168, 108804.
27. Yuan, W.; Yu, N.; Li, L.; Fang, Y. Heat transfer analysis in multi-layered materials with interfacial thermal resistance. Compos. Struct. 2022, 293, 115728.
28. Huang, H. C.; Usmani, A. S. Introduction. In: Finite element analysis for heat transfer. London: Springer; 1994. p. 1-5.
29. Bergheau, J. M.; Fortunier, R. Finite element simulation of heat transfer. John Wiley & Sons: 2013.
30. Hu, M.; Yang, Z. Perspective on multi-scale simulation of thermal transport in solids and interfaces. Phys. Chem. Chem. Phys. 2021, 23, 1785-801.
31. Ma, T.; Chakraborty, P.; Guo, X.; Cao, L.; Wang, Y. First-principles modeling of thermal transport in materials: achievements, opportunities, and challenges. Int. J. Thermophys. 2020, 41, 2583.
32. Łach, Ł.; Svyetlichnyy, D. Advances in numerical modeling for heat transfer and thermal management: a review of computational approaches and environmental impacts. Energies 2025, 18, 1302.
33. Yao, H.; Gao, Y.; Liu, Y. FEA-Net: A physics-guided data-driven model for efficient mechanical response prediction. Comput. Methods. Appl. Mech. Eng. 2020, 363, 112892.
34. Zhu, C.; Bamidele, E. A.; Shen, X.; Zhu, G.; Li, B. Machine learning aided design and optimization of thermal metamaterials. Chem. Rev. 2024, 124, 4258-331.
35. de Pablo, J. J.; Jackson, N. E.; Webb, M. A.; et al. New frontiers for the materials genome initiative. npj. Comput. Mater. 2019, 5, 173.
36. Pollice, R.; Dos Passos Gomes, G.; Aldeghi, M.; et al. Data-driven strategies for accelerated materials design. Acc. Chem. Res. 2021, 54, 849-60.
37. Jha, D.; Ward, L.; Paul, A.; et al. ElemNet: deep learning the chemistry of materials from only elemental composition. Sci. Rep. 2018, 8, 17593.
38. Chen, Q.; Jia, R.; Pang, S. Deep long short-term memory neural network for accelerated elastoplastic analysis of heterogeneous materials: an integrated data-driven surrogate approach. Compos. Struct. 2021, 264, 113688.
39. Sun, W.; Bartel, C. J.; Arca, E.; et al. A map of the inorganic ternary metal nitrides. Nat. Mater. 2019, 18, 732-9.
40. Liu, B.; Wang, D.; Avdeev, M.; Shi, S.; Yang, J.; Zhang, W. High-throughput computational screening of Li-containing fluorides for battery cathode coatings. ACS. Sustainable. Chem. Eng. 2020, 8, 948-57.
41. Hautier, G.; Jain, A.; Chen, H.; Moore, C.; Ong, S. P.; Ceder, G. Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations. J. Mater. Chem. 2011, 21, 17147.
42. Mounet, N.; Gibertini, M.; Schwaller, P.; et al. Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nat. Nanotechnol. 2018, 13, 246-52.
43. Huang, W.; Martin, P.; Zhuang, H. L. Machine-learning phase prediction of high-entropy alloys. Acta. Mater. 2019, 169, 225-36.
44. Islam, N.; Huang, W.; Zhuang, H. L. Machine learning for phase selection in multi-principal element alloys. Comput. Mater. Sci. 2018, 150, 230-5.
45. Shin, S.; Ko, B.; So, H. Noncontact thermal mapping method based on local temperature data using deep neural network regression. Int. J. Heat. Mass. Transfer. 2022, 183, 122236.
46. Ouyang, R.; Curtarolo, S.; Ahmetcik, E.; Scheffler, M.; Ghiringhelli, L. M. SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys. Rev. Mater. 2018, 2, 083802.
47. Bartel, C. J.; Sutton, C.; Goldsmith, B. R.; et al. New tolerance factor to predict the stability of perovskite oxides and halides. Sci. Adv. 2019, 5, eaav0693.
48. Liu, X.; Guo, Y.; Liu, W.; Zeng, L. Numerical simulation research on three dimensional ablative thermal response of charring ablators. J. Astronaut. 2016, 37, 1150-6. (in Chinese).
49. Dewey, W. C. Arrhenius relationships from the molecule and cell to the clinic. Int. J. Hyperthermia. 1994, 10, 457-83.
50. Peleg, M.; Normand, M. D.; Corradini, M. G. The Arrhenius equation revisited. Crit. Rev. Food. Sci. Nutr. 2012, 52, 830-51.
51. Appleton, J. P.; Bray, K. N. C. The conservation equations for a non-equilibrium plasma. J. Fluid. Mech. 1964, 20, 659-72.
52. Wheatcraft, S. W.; Meerschaert, M. M. Fractional conservation of mass. Adv. Water. Resources. 2008, 31, 1377-81.
53. Hubbert, M. K. Darcy’s law and the field equations of the flow of underground fluids. Trans. AIME. 1956, 207, 222-39.
54. Wang, J.; Xie, H.; Wang, Y.; Ouyang, R. Distilling accurate descriptors from multi-source experimental data for discovering highly active perovskite OER catalysts. J. Am. Chem. Soc. 2023, 145, 11457-65.
57. Minsky, M.; Papert, S. A. Perceptrons: an introduction to computational geometry. MIT Press: 2017.
58. Nwankpa, C.; Ijomah, W.; Gachagan, A.; Marshall, S. Activation functions: comparison of trends in practice and research for deep learning. arXiv2018, arXiv:1811.03378. Available online: https://doi.org/10.48550/arXiv.1811.03378. (accessed 11 Apr 2025)
59. Baydin, A. G.; Pearlmutter, B. A.; Radul, A. A.; Siskind, J. M. Automatic differentiation in machine learning: a survey. arXiv2015, arXiv:1502.05767. Available online: https://doi.org/10.48550/arXiv.1502.05767. (accessed 11 Apr 2025)
60. Agrawal, A.; Choudhary, A. Deep materials informatics: applications of deep learning in materials science. MRS. Commun. 2019, 9, 779-92.
61. Permann, C. J.; Gaston, D. R.; Andrš, D.; et al. MOOSE: enabling massively parallel multiphysics simulation. SoftwareX 2020, 11, 100430.
62. Zhang, C.; Liu, X.; Chen, J.; Hu, X.; Guo, Y.; Cui, P. Study on uncertainty propagation analysis method in ablative thermal response calculation. J. Astronautics. 2020, 41, 1401-9. (in Chinese).
63. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B. 1996, 58, 267-88.
66. Ye, S.; Senftle, T. P.; Li, M. Operator-induced structural variable selection for identifying materials genes. J. Am. Stat. Assoc. 2024, 119, 81-94.
67. Sandberg, J.; Voigtmann, T.; Devijver, E.; Jakse, N. Feature selection for high-dimensional neural network potentials with the adaptive group lasso. Mach. Learn. Sci. Technol. 2024, 5, 025043.
68. Wang, P.; Zhao, S.; Zhou, C.; et al. Simple formula learned via machine learning for creep rupture life prediction of high-temperature titanium alloys. J. Mater. Inf. 2024, 4, 24.
69. Fox, J.; Weisberg, S. An R companion to applied regression. Sage publications: 2018. https://uk.sagepub.com/en-gb/eur/an-r-companion-to-applied-regression/book246125. (accessed 11 Apr 2025).
