暗网禁区 学术报告[2026]053号

（高水平大学建设系列报告1312号）

报告题目：Integrating Poroelastic Finite Element Simulations and Machine Learning for Mechanical Parameter Identification of Porcine Lungs

报告人：蔡明超 教授（美国摩根州立大学）

报告时间：2026年6月8日10:00-11:30

报告地点：粤海校区汇星楼514会议室

报告摘要：Accurate and rapid characterization of lung mechanics remains a major challenge in the management of respiratory disease. Physics-informed poroelastic finite-element (FE) models can capture detailed tissue–airflow interactions, but their substantial computational expense precludes real-time clinical deployment. Conversely, lumped-parameter compartment models are routinely used at the bedside for fast assessment of disease severity, yet they provide limited mechanistic insight and no spatial resolution. In this work, using porcine-specific data, we integrate multi-fidelity poroelastic FE simulations with machine learning to enable fast, uncertainty-aware estimation of respiratory system compliance and resistance.

报告人简介：蔡明超教授, 美国马里兰摩根州立大学教授，博导。主要从事数值分析，流体,弹性和生物力学的计算.侧重多物理和多区域问题的建模与计算. 在SINUM, SISC, Math. Comput., 和 Journal of Biomechanical Engineering, ASME, J. Biomechanics等有影响力的SCI期刊上发表论文40多篇，出版book chapters 2部，做大会或邀请报告共 80多次，MathSciNet及40多种计算数学, 计算物理，计算生物力学杂志评审专家, 组织并主持过学术会议：CBMS conference: Deep Learning and Numerical PDEs (2023).

邀请人：刘强

暗网禁区

2026年6月3日