journal article·2024-01Open access

Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites

Bokai Liu, Weizhuo Lu, Thomas Olofsson, Xiaoying Zhuang, Timon Rabczuk

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: Thermal properties of materials · Composite Material Mechanics · Probabilistic and Robust Engineering Design

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (54)

Show source-provided references
1. Graphene-based composite materials10.1038/nature04969 ↗
2. Graphene-based composites10.1039/C1CS15078B ↗
3. Superior thermal conductivity of single-layer graphene10.1021/nl0731872 ↗
4. Extremely high thermal conductivity of graphene: Prospects for thermal management applications in nanoelectronic circuits10.1063/1.2907977 ↗
5. Nanoporous graphene: A 2D semiconductor with anisotropic mechanical, optical and thermal conduction properties10.1016/j.carbon.2019.03.018 ↗
6. Modeling of two-phase random composite materials by finite element, Mori–Tanaka and strong contrast methods10.1016/j.compositesb.2012.05.015 ↗
7. Mesoscopic and multiscale modelling in materials10.1038/s41563-020-00913-0 ↗
8. Experimental investigations on graphene oxide/rubber composite thermal conductivity10.1038/s41598-020-72633-z ↗
9. Thermal conductivity enhancement of ethylene glycol and water with graphene nanoplatelets10.1016/j.tca.2016.09.002 ↗
10. Highly thermally conductive polypropylene/graphene composites for thermal management10.1016/j.compositesa.2020.105912 ↗
11. Estimating Young’s modulus of graphene/polymer composites using stochastic multi-scale modeling10.1016/j.compositesb.2019.05.053 ↗
12. Vibration and buckling optimization of functionally graded porous microplates using BCMO-ANN algorithm10.1016/j.tws.2022.110267 ↗
13. An effective approach for VARANS-VOF modelling interactions of wave and perforated breakwater using gradient boosting decision tree algorithm10.1016/j.oceaneng.2022.113398 ↗
14. Automatic laser profile recognition and fast tracking for structured light measurement using deep learning and template matching10.1016/j.measurement.2020.108362 ↗
15. Stochastic multi-scale modeling of CNT/polymer composites10.1016/j.commatsci.2010.08.036 ↗
16. Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs)10.1016/j.compositesb.2013.11.014 ↗
17. Stochastic multiscale modeling of heat conductivity of Polymeric clay nanocomposites10.1016/j.mechmat.2019.103280 ↗
18. Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites10.1016/j.compscitech.2022.109425 ↗
19. A stochastic multiscale method for the prediction of the thermal conductivity of Polymer nanocomposites through hybrid machine learning algorithms10.1016/j.compstruct.2021.114269 ↗
20. Stochastic full-range multiscale modeling of thermal conductivity of polymeric carbon nanotubes composites: A machine learning approach10.1016/j.compstruct.2022.115393 ↗
21. Al-DeMat: A web-based expert system platform for computationally expensive models in materials design10.1016/j.advengsoft.2022.103398 ↗
22. Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design10.1504/IJHM.2022.127037 ↗
23. A machine learning-based surrogate model to approximate optimal building retrofit solutions10.1016/j.apenergy.2020.116024 ↗
24. Prediction of bending strength of glass fiber reinforced methacrylate-based pipeline UV-CIPP rehabilitation materials based on machine learning10.1016/j.tust.2023.105319 ↗
25. Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media
26. A data-driven approach based on long short-term memory and hidden Markov model for crack propagation prediction10.1016/j.engfracmech.2020.107085 ↗
27. Improved ANN technique combined with Jaya algorithm for crack identification in plates using XIGA and experimental analysis10.1016/j.tafmec.2020.102554 ↗
28. A modified transmissibility indicator and artificial neural network for damage identification and quantification in laminated composite structures10.1016/j.compstruct.2020.112497 ↗
29. Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites10.1016/j.compstruct.2021.113917 ↗
30. A novel machine-learning based on the global search techniques using vectorized data for damage detection in structures10.1016/j.ijengsci.2020.103376 ↗
31. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI10.1016/j.inffus.2019.12.012 ↗
32. A unified approach to interpreting model predictions
33. Explaining by removing: A unified framework for model explanation
34. Small sample sensitivity analysis techniques for computer models. with an application to risk assessment10.1080/03610928008827996 ↗
35. The role of Latin Hypercube Sampling method in reliability engineering
36. Keitel · 2012 · 390
37. Dimensional crossover of thermal transport in few-layer graphene10.1038/nmat2753 ↗
38. Thermal and electrical conductivity of single-and multi-walled carbon nanotube-epoxy composites10.1016/j.compscitech.2005.10.016 ↗
39. Energy dissipation in graphene field-effect transistors10.1021/nl803883h ↗
40. Heat transfer in high volume fraction CNT nanocomposites: Effects of inter-nanotube thermal resistance10.1016/j.cplett.2011.04.005 ↗
41. Random forests10.1023/A:1010933404324 ↗
42. Kuhn · 2013
43. Greedy function approximation: a gradient boosting machine
44. Artificial neural networks: A tutorial10.1109/2.485891 ↗
45. Molnar · 2020
46. Definitions, methods, and applications in interpretable machine learning10.1073/pnas.1900654116 ↗
47. Slack D, Hilgard S, Jia E, Singh S, Lakkaraju H. Fooling lime and shap: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM conference on AI, ethics, and society. 2020, p. 180–6.10.1145/3375627.3375830 ↗
48. Lundberg · 2018
49. Explaining prediction models and individual predictions with feature contributions10.1007/s10115-013-0679-x ↗
50. Enhanced thermal conductivity of epoxy–graphene composites by using non-oxidized graphene flakes with non-covalent functionalization10.1002/adma.201202736 ↗
51. Mechanical properties and thermal conductivity of graphene nanoplatelet/epoxy composites10.1007/s10853-014-8665-6 ↗
52. Grafting of epoxy chains onto graphene oxide for epoxy composites with improved mechanical and thermal properties10.1016/j.carbon.2013.12.050 ↗
53. Super-high thermal conductivity of polyamide-6/graphene-graphene oxide composites through in situ polymerization10.1177/0954008316655861 ↗
54. The influence of carbon nanotube aspect ratio on thermal conductivity enhancement in nanotube–polymer composites10.1115/1.4025047 ↗