报告人：黄晟林 博士

美国宾夕法尼亚大学机械工程与应用力学系

时间：9月6日 (周三) 下午14:00—15:00

地点：88858cc永利官网1号楼210会议室

内容简介:

Non-equilibrium phenomena are ubiquitous across material systems and of great technological relevance. Examples of such phenomena include diffusion processes in liquid and gases, viscoelasticity and plasticity in solids, and rheological behavior of colloidal and granular media. However, the understanding of non-equilibrium phenomena remains in its infancy compared with classical equilibrium thermodynamics and statistical mechanics from both theoretical and computational aspects. This talk leverages recent advances in non-equilibrium physics, together with emerging machine learning techniques, to develop theoretical and computational paradigms for learning continuum evolution equations using data-driven methods. First, we present a strategy for continuum model discovery using fluctuation theorems to identify the reversible (elastic) and irreversible (dissipative) response. Second, we propose a machine learning architecture called Variational Onsager Neural Networks (VONNs) to learn thermodynamically consistent non-equilibrium PDEs. Thirdly, we develop a machine learning framework to coarse-grain dissipative PDEs from stochastic particle dynamics. Lastly, we introduce a statistical mechanics framework with quantified uncertainty to extrapolate material behavior to different loadings or material systems.

报告人简介：

黄晟林，先后于2015年、2018年获得88858cc永利官网力学系学士、硕士学位，此后于2023年获得美国宾夕法尼亚大学机械工程与应用力学系博士学位。博士期间师从Celia Reina教授，利用连续介质力学、统计物理、机器学习等方法，研究、理解并预测非平衡态系统的力学行为。黄博士曾获得由美国理论与应用力学国家委员会（USNC/TAM）颁发的Thomas Hughes Fellowship，该奖旨在奖励力学领域中领先的青年研究者。黄博士还荣获宾夕法尼亚大学机械工程与应用力学系研究生最高奖项John Goff奖。黄博士目前担任美国计算力学协会学生分会（USACM Student Chapter）的执行委员，已在JMPS、PRL、PNAS、JFM等学术期刊上发表多篇文章。

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