| A stochastic multiscale method for the prediction of the thermal conductivity of Polymer nanocomposites through hybrid machine learning algorithms B Liu, N Vu-Bac, T Rabczuk Composite Structures 273, 114269, 2021 | 65 | 2021 |
| Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites B Liu, N Vu-Bac, X Zhuang, X Fu, T Rabczuk Composites Science and Technology 224, 109425, 2022 | 55 | 2022 |
| Stochastic multiscale modeling of heat conductivity of Polymeric clay nanocomposites B Liu, N Vu-Bac, X Zhuang, T Rabczuk Mechanics of Materials 142, 103280, 2020 | 50 | 2020 |
| Stochastic full-range multiscale modeling of thermal conductivity of Polymeric carbon nanotubes composites: A machine learning approach B Liu, N Vu-Bac, X Zhuang, X Fu, T Rabczuk Composite Structures 289, 115393, 2022 | 41 | 2022 |
| Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design B Liu, W Lu International Journal of Hydromechatronics 5 (4), 336-365, 2022 | 27 | 2022 |
| Al-DeMat: A web-based expert system platform for computationally expensive models in materials design B Liu, N Vu-Bac, X Zhuang, W Lu, X Fu, T Rabczuk Advances in Engineering Software 176, 103398, 2023 | 10 | 2023 |
| Multi-scale modeling in thermal conductivity of Polyurethane incorporated with Phase Change Materials using Physics-Informed Neural Networks B Liu, Y Wang, T Rabczuk, T Olofsson, W Lu Renewable Energy 220, 119565, 2024 | 9 | 2024 |
| Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: A case study in northern Sweden B Liu, SR Penaka, W Lu, K Feng, A Rebbling, T Olofsson Technology in Society 75, 102347, 2023 | 7 | 2023 |
| Prediction of bending strength of glass fiber reinforced methacrylate-based pipeline UV-CIPP rehabilitation materials based on machine learning Y Xia, C Zhang, C Wang, H Liu, X Sang, R Liu, P Zhao, G An, H Fang, ... Tunnelling and Underground Space Technology 140, 105319, 2023 | 7 | 2023 |
| Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites B Liu, W Lu, T Olofsson, X Zhuang, T Rabczuk Composite Structures 327, 117601, 2024 | 5 | 2024 |
| HomoGenius: a Foundation Model of Homogenization for Rapid Prediction of Effective Mechanical Properties using Neural Operators Y Wang, X Li, Z Yan, Y Du, J Bai, B Liu, T Rabczuk, Y Liu arXiv preprint arXiv:2404.07943, 2024 | | 2024 |
| Stochastic multiscale modeling of nanopolymer composites based on data-driven techniques B Liu Young Scholars Forum in Advanced Composite Materials and Structural …, 2023 | | 2023 |
| Multiscale modeling of Heat transfer in Polyurethane-Phase Change Materials composites B Liu, W Lu, O Thomas Young Investigators Symposium Umeå 2023, October 3rd, 2023, Umeå Sweden., 2023 | | 2023 |
| Multiscale modeling of thermal properties in Polyurethane incorporated with phase change materials composites: A case study B Liu, W Lu, X Hu, C Zhang, C Wang, Y Qu, T Olofsson arXiv preprint arXiv:2308.13123, 2023 | | 2023 |
| Stochastic multiscale modeling of polymer nanocomposites based on integrated machine learning B Liu Conference Proceedings of Data Driven Computing and Machine Learning in …, 2023 | | 2023 |
| Stochastic multiscale modeling of polymer composites based on ensemble learning B Liu Symposium on Intelligent Simulation and Control for Multiscale Mechanics, 2022 | | 2022 |
| Stochastic multiscale modeling of polymeric nanocomposites using Data-driven techniques B Liu PhD Dissertation. Bauhaus-Universität Weimar. ISM-Bericht 3/2022 ISSN: 1610-7381, 2022 | | 2022 |
| Enhancing Thermal Conductivity Modeling of Polyurethane with Phase Change Materials via Physics-Informed Neural Networks at Multiple Scales B Liu, Y Wang, T Olofsson, W Lu | | |
| Deep Learning-Based Multi-Scale Modeling of Thermal Conductivity in Polyurethane with Phase Change Materials Via Neural Operators B Liu, Y Wang, Y Liu, K Feng, T Rabczuk, T Olofsson, J Bai, W Lu Available at SSRN 4702962, 0 | | |
| Comparison of Mive and Survey for Observing Thermal-Driven Occupant Behavior: Experiment-Based Evidence C Chokwitthaya, K Feng, B Liu, W Lu Available at SSRN 4544958, 0 | | |
Timon RabczukBauhaus University WeimarVerified email at uni-weimar.de
