学术报告

Learning Multiscale Models Using Nonlocal Upscaling Techniques

阅读次数:578

题目:Learning Multiscale Models Using Nonlocal Upscaling Techniques
报告人:Prof. Eric Chung (香港中文大学)
地点:致远楼101室
时间:2019年12月16日  14:00-15:00
摘要:In this talk, we present a novel nonlocal nonlinear coarse grid
approximation using a machine learning algorithm.
Multiscale models for complex nonlinear systems require nonlocal multicontinua approaches. These rigorous techniques require complex local computations, which involve solving local problems in oversampled regions subject to constraints. The solutions of these local problems can be replaced by solving original problem on a coarse (oversampled) region for many input parameters (boundary and source terms) and computing effective properties derived by nonlinear nonlocal multicontinua approaches. The effective properties depend on many variables (oversampled region and the number of continua), thus their calculations require some type of machine learning techniques. 
We present results for two model problems in heterogeneous and fractured porous media and show that the presented method is highly accurate and provides fast coarse grid calculations.

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