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Learning Operators - Lecture 1

Offered By: Centre International de Rencontres Mathématiques via YouTube

Tags

Machine Learning Courses Neural Networks Courses Mathematical Analysis Courses Scientific Computing Courses Numerical Methods Courses Computational Mathematics Courses Approximation Theory Courses

Course Description

Overview

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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.

Syllabus

Introduction
What are operators
Traditional methods
Examples
Issues
Objective
Setting
Neural Networks
Approximation Error
Random Sampling
Practice
Downstream tasks


Taught by

Centre International de Rencontres Mathématiques

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