Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/1901.07758 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910753271840768 |
|---|---|
| author | Xu, Kailai Darve, Eric |
| author_facet | Xu, Kailai Darve, Eric |
| contents | We proposed a framework for solving inverse problems in differential equations based on neural networks and automatic differentiation. Neural networks are used to approximate hidden fields. We analyze the source of errors in the framework and derive an error estimate for a model diffusion equation problem. Besides, we propose a way for sensitivity analysis, utilizing the automatic differentiation mechanism embedded in the framework. It frees people from the tedious and error-prone process of deriving the gradients. Numerical examples exhibit consistency with the convergence analysis and error saturation is noteworthily predicted. We also demonstrate the unique benefits neural networks offer at the same time: universal approximation ability, regularizing the solution, bypassing the curse of dimensionality and leveraging efficient computing frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1901_07758 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | The Neural Network Approach to Inverse Problems in Differential Equations Xu, Kailai Darve, Eric Numerical Analysis We proposed a framework for solving inverse problems in differential equations based on neural networks and automatic differentiation. Neural networks are used to approximate hidden fields. We analyze the source of errors in the framework and derive an error estimate for a model diffusion equation problem. Besides, we propose a way for sensitivity analysis, utilizing the automatic differentiation mechanism embedded in the framework. It frees people from the tedious and error-prone process of deriving the gradients. Numerical examples exhibit consistency with the convergence analysis and error saturation is noteworthily predicted. We also demonstrate the unique benefits neural networks offer at the same time: universal approximation ability, regularizing the solution, bypassing the curse of dimensionality and leveraging efficient computing frameworks. |
| title | The Neural Network Approach to Inverse Problems in Differential Equations |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/1901.07758 |