Machine learning-based prediction and optimization of performances of porous ZrO2/Al2O3 ceramics: a few-shot learning approach
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Fluoride Effects and Removal · Inorganic Fluorides and Related Compounds · Groundwater and Isotope Geochemistry
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (62)
Show source-provided references
1. Water fluoridation: a critical review of the physiological effects of ingested fluoride as a public health intervention10.1155/2014/293019 ↗
2. A review of the emerging ceramic adsorbents for defluoridation of groundwater10.1016/j.jwpe.2020.101365 ↗
3. Fluoride ions vs removal technologies: a study10.1016/j.arabjc.2014.06.005 ↗
4. Microstructure and mechanical properties of 3D printed porous Al2O3–ZrO2 laminated ceramics with tailored porosity10.1016/j.ceramint.2023.08.057 ↗
5. Porous ceramics based on high-thermal-stability Al2O3–ZrO2 nanofibers for thermal insulation and sound absorption applications10.1016/j.ceramint.2023.07.048 ↗
6. An efficient PDA/Al2O3 nanosheets reinforced ultra-thin ZrO2 coating with attractive anti-corrosion and deuterium resistance property10.1016/j.cej.2022.138307 ↗
7. Catalytic hydrolysis of CCl2F2 by catalyst Al2O3–ZrO210.1007/s10562-022-04186-x ↗
8. Microstructure and properties of hierarchical porous ZrO2/Al2O3 ceramics prepared by gel-foaming method10.1007/s41779-023-00883-x ↗
9. Microwave hybrid sintering of Al2O3 and Al2O3–ZrO2 composites, and effects of ZrO2 crystal structure on mechanical properties, thermal properties, and sintering kinetics10.1016/j.ceramint.2019.12.147 ↗
10. Synthesis of (ZrO2-Al2O3)/GO nanocomposite by sonochemical method and the mechanism analysis of its high defluoridation10.1016/j.jhazmat.2019.120954 ↗
11. A review of pore-forming agents on the structures, porosities, and mechanical properties of porous ceramics
12. Preparation of ZTA composite ceramics derived from sol–gel method by DIW printing10.1111/ijac.14811 ↗
13. Freeze-cast porous Al2O3 ceramics strengthened by up to 80% ceramics fibers10.1016/j.ceramint.2021.12.185 ↗
14. Understanding machine learning principles: learning, inference, generalization, and computational learning theory10.3390/math13030451 ↗
15. Different applications of machine learning approaches in materials science and engineering: comprehensive review10.1016/j.engappai.2024.108783 ↗
16. Machine learning in energy storage material discovery and performance prediction10.1016/j.cej.2024.152294 ↗
17. State-of-the-art review on various applications of machine learning techniques in materials science and engineering10.1016/j.ces.2024.121147 ↗
18. Predictive modeling of high-entropy alloys and amorphous metallic alloys using machine learning10.1021/acs.jcim.4c00873 ↗
19. A novel deep learning strategy to optimize al₂o₃–SiO₂ porous ceramics with phosphate tailings as raw material10.1016/j.ceramint.2024.06.314 ↗
20. Employing machine learning algorithm for properties of wood ceramics prediction: a case study of ammonia nitrogen adsorption capacity, apparent porosity, surface hardness and burn-off for wood ceramics10.1016/j.ceramint.2024.01.298 ↗
21. Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: a review10.1016/j.jmst.2024.01.086 ↗
22. Identifying MOFs for electrochemical energy storage via density functional theory and machine learning10.1038/s41524-025-01590-w ↗
23. A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks10.1038/s42256-023-00628-2 ↗
24. Review of transfer learning in modeling additive manufacturing processes
25. Small data machine learning in materials science10.1038/s41524-023-01000-z ↗
26. A comprehensive survey of continual learning: theory, method and application10.1109/TPAMI.2024.3367329 ↗
27. A definition of continual reinforcement learning
28. SCTCN-LightGBM: a hybrid learning method via transposed dimensionality-reduction convolution for loading measurement of industrial material10.1080/09540091.2023.2278275 ↗
29. A comparative study of LightGBM, XGBoost, and GEP models in shear strength management of SFRC-SBWs
30. Advances in machine learning-and artificial intelligence-assisted material design of steels10.1007/s12613-022-2595-0 ↗
31. Support vector machine (SVM) application for uniaxial compression strength (UCS) prediction: a case study for Maragheh limestone10.3390/app13042217 ↗
32. Web data mining with organized contents using Naive Bayes algorithm
33. Comparison of k-nearest neighbor & artificial neural network prediction in the mechanical properties of aluminum alloys10.1016/j.matpr.2023.09.111 ↗
34. A survey on transfer learning10.1109/TKDE.2009.191 ↗
35. How transferable are features in deep neural networks
36. Model-agnostic meta-learning for fast adaptation of deep networks
37. Obst · 2021
38. Few-shot regression with differentiable reference model10.1016/j.ins.2023.120010 ↗
39. From explanations to feature selection: assessing SHAP values as feature selection mechanism
40. Permutation importance: a corrected feature importance measure10.1093/bioinformatics/btq134 ↗
41. Hybrid-recursive feature elimination for efficient feature selection10.3390/app10093211 ↗
42. Han · 2021
43. A composite Bayesian optimisation framework for material and structural design
44. Bayesian optimisation for efficient material discovery: a mini review10.1039/D2NR07147A ↗
45. A novel feature engineered CatBoost-based supervised machine learning framework for electricity theft detection10.1016/j.egyr.2021.07.008 ↗
46. XGBoost to enhance learner performance prediction
47. An improved random forest based on the classification accuracy and correlation measurement of decision trees10.1016/j.eswa.2023.121549 ↗
48. Extreme gradient boosting algorithm with machine learning: a review10.25007/ajnu.v12n2a1612 ↗
49. Elastic net regularization paths for all generalized linear models10.18637/jss.v106.i01 ↗
50. On LASSO for predictive regression10.1016/j.jeconom.2021.02.002 ↗
51. Support vector machine in structural reliability analysis: a review10.1016/j.ress.2023.109126 ↗
52. A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting10.1007/s40745-021-00344-x ↗
53. A comparative analysis of k-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning
54. Ultrahigh strength ZTA ceramic foams with large amounts of intragranular structures resulting from dual-phase sol10.1016/j.ceramint.2024.01.442 ↗
55. Porous Al2O3 ceramics with spontaneously formed pores and enhanced strength prepared by indirect selective laser sintering combined with reaction bonding10.1016/j.ceramint.2020.03.052 ↗
56. Ceramic foams with highly open channel structure from direct foaming method in combination with hollow spheres as pore-former10.1080/21870764.2020.1847427 ↗
57. Effects of sintering temperature and V₂O₅ additive on the properties of SiC–Al2O3 ceramic foams10.1016/j.jallcom.2017.10.170 ↗
58. Impacts of inclusion of additives on physical, microstructural, and mechanical properties of alumina and zirconia toughened alumina (ZTA) ceramic composite: a review10.1016/j.matpr.2022.02.481 ↗
59. Phase composition, microstructure, and mechanical properties of ZTA ceramics prepared by gelcasting using multi-gradation spherical powders
60. Fabrication and characterization of alumina and zirconia-toughened alumina porous structures10.1016/j.ceramint.2023.03.310 ↗
61. Multi-objective optimization in machine learning assisted materials design and discovery
62. Multi-objective optimization of lightweight, energy absorption, and heat transfer in gradient porous metals via deep learning and genetic algorithm