A Mathematical Guide to Operator Learning
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866908341331034112 |
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| author | Boullé, Nicolas Townsend, Alex |
| author_facet | Boullé, Nicolas Townsend, Alex |
| contents | Operator learning aims to discover properties of an underlying dynamical system or partial differential equation (PDE) from data. Here, we present a step-by-step guide to operator learning. We explain the types of problems and PDEs amenable to operator learning, discuss various neural network architectures, and explain how to employ numerical PDE solvers effectively. We also give advice on how to create and manage training data and conduct optimization. We offer intuition behind the various neural network architectures employed in operator learning by motivating them from the point-of-view of numerical linear algebra. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_14688 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Mathematical Guide to Operator Learning Boullé, Nicolas Townsend, Alex Numerical Analysis Artificial Intelligence Machine Learning Operator learning aims to discover properties of an underlying dynamical system or partial differential equation (PDE) from data. Here, we present a step-by-step guide to operator learning. We explain the types of problems and PDEs amenable to operator learning, discuss various neural network architectures, and explain how to employ numerical PDE solvers effectively. We also give advice on how to create and manage training data and conduct optimization. We offer intuition behind the various neural network architectures employed in operator learning by motivating them from the point-of-view of numerical linear algebra. |
| title | A Mathematical Guide to Operator Learning |
| topic | Numerical Analysis Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2312.14688 |