機器學習在偏微分方程式的應用

In this course, we will study the machine learning algorithms for numerically solving a wide of class of partial differential equations, in particular for high-dimensional partial differential equations such as Hamilton-Jacobi-Bellman equations, Black-Scholes equations, etc.. The topics will include Monte Carlo methods for high-dimensional partial differential equations, Neural Network theory, and learning algorithms for PDEs.

You might need some background knowledge in linear algebra, probability, numerical analysis and partial differential equations and capable of using python or Matlab.

Basically, we will use blackboard or online lecture. This depends on the evolution of the COVID-19 pandemic.

The grading policy will be based on the following: 1. homework assignments (60%) 2. Final project and presentation (40%)

Basically, we will use my lecture notes.

地點

SA 214

時間

We will meet at 10:10~11:00 on Mondays and at 13:20~15:10 on Thursdays.

聯絡方式

Please email me first at mcshiue@nycu.edu.tw