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1
Introduction
2
What are operators
3
Traditional methods
4
Examples
5
Issues
6
Objective
7
Setting
8
Neural Networks
9
Approximation Error
10
Random Sampling
11
Practice
12
Downstream tasks
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the fundamentals of learning operators in this lecture from the CEMRACS: Scientific Machine Learning thematic meeting. Delve into the concept of operators, traditional methods, and their limitations. Discover the objectives and settings for learning operators using neural networks. Examine approximation errors and random sampling techniques. Gain practical insights into downstream tasks related to operator learning. Benefit from chapter markers, keywords, abstracts, and bibliographies to navigate the content efficiently. Access this comprehensive mathematical resource as part of CIRM's Audiovisual Mathematics Library, featuring talks from renowned mathematicians worldwide.

Learning Operators - Lecture 1

Centre International de Rencontres Mathématiques
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