报告人：程功（美国达特茅斯学院 研究员）

时间：2025年8月14日（周四） 10:00-11:30

地点：空间信息中心204会议室

报告简介：

Predicting the future contributions of the ice sheets to sea level rise remains a significant challenge due to our limited understanding of key physical processes (e.g., basal friction, ice rheology, calving dynamics) and the lack of observations of critical model inputs (e.g., bed topography). Traditional numerical models typically rely on data assimilation methods to estimate these variables by solving inverse problems based on conservation laws of mass, momentum, and energy. However, these methods are not versatile and require extensive code development to incorporate new physics. Moreover, their dependence on data alignment within computational grids hampers their adaptability. To address these limitations, we developed PINNICLE (Physics-Informed Neural Networks for Ice and CLimatE), an open-source Python library dedicated to ice sheet modeling. PINNICLE seamlessly integrates observational data and physical laws, facilitating the solution of both forward and inverse problems within a single framework. We describe here the implementation of PINNICLE and showcase this library with examples across the Greenland and Antarctic ice sheets for a range of forward and inverse problems.

报告人简介：

程功，瑞典乌普萨拉大学博士，曾在加州大学尔湾分校从事博士后工作，目前担任美国达特茅斯学院地球科学系研究员和讲师。程博士致力于利用数值方法研究冰盖动力过程，研究兴趣包括机器学习在冰川学中的应用，数据同化与反问题，偏微分方程有限元求解，冰-海/冰-岩相互作用等，主持和参与多项关于冰冻圈模拟与观测的研究项目，同时也是 The Cryosphere杂志的编委。