应水利与交通学院邀请，瑞典于默奥大学Bokai Liu研究员，为广大师生做专题学术报告。欢迎广大师生积极参加！

专家简介：Bokai Liu研究员，瑞典于默奥大学应用物理与电子系智能人与建筑交互实验室Senior Researcher（PI），还担任瑞典北极中心可再生能源领域的联合研究员。 其研究方向集中在随机多尺度建模、数据驱动建模和计算复合材料设计。 其主持的项目由 J Gust Richert stiftelse SWECO、瑞典 Kempe 基金会和欧盟 Horizon 2020资助和支持。于2022 年在德国魏玛包豪斯大学获得博士学位（导师：Prof. Dr.-Ing. Timon Rabczuk），并且获得了由DAAD-STIBET 颁发的博士学位完成奖学金。 其担任International Journal of Impact Engineering、Engineering with Computers等多个顶级期刊审稿人，还担任American Journal of Electrical Power and Energy Systems编委。 与此同时也是国际室内空气质量与气候学会(ISIAQ)、中国仿真学会人工智能仿真技术专业委员会、中国宇航学会会员。

Abstract: Extensive research and development have been dedicated to nano-reinforced polymer composites, owing to their exceptional physical and chemical properties. Recent studies have focused on quantifying the impact of nanofillers on the properties of these composites. Properties such as macroscopic thermal conductivity play a vital role in various engineering applications, including aerospace engineering, automotive industry, energy storage equipment, and electronic devices. The composition of the embedded polymeric filler in the composite matrix significantly influences the overall macroscopic properties of the material. However, previous studies primarily relied on deterministic models that disregarded uncertainties and did not account for the presence of uncertainties in these materials. Consequently, the predicted results deviated from the experimental findings. Moreover, the computational costs associated with stochastic multiscale modeling are high, prompting the use of alternative methods to propagate uncertain parameters across scales. With advancements in high-performance computing and artificial intelligence, data-driven techniques like sensitivity analysis and machine learning have gained popularity as a modeling tool in numerous applications. Machine learning (ML) is often employed to construct surrogate models by establishing mappings between specific rules and algorithms to build input-output models using available data. ML models are particularly useful for nonlinear inputs, especially when sufficient data are accessible to establish robust relationships. In this study, we propose a stochastic multiscale approach based on data-driven techniques to quantify the influence of different input parameters on output parameters as well as predict the macroscopic thermal conductivity of nano-reinforced polymer composites. We developed data-driven based Multi-scale stochastic modeling with uncertainty analysis and machine learning methods including sensitivity analysis, Hybrid Machine Learning, Stochastic Integrated Machine Learning. These models are integral to the stochastic modeling process, allowing us to construct representations of all uncertain input variables and the desired output parameterization, specifically the macroscopic thermal conductivity of the composite material. To find the global optimum and significantly reduce computational costs, we employ Particle Swarm Optimization (PSO) for hyperparameter tuning. We also conduct an analysis of the computational costs and model complexity, examining the advantages and disadvantages of each method. The results demonstrate that the proposed stochastic ensemble machine learning method, which considers uncertainties, exhibits excellent performance. This method plays a crucial role in computational modeling, aiding in the design of new composite materials for applications related to thermal management.