The Weak Form Is Stronger Than You Think
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916388576165888 |
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| author | Messenger, Daniel A. Tran, April Dukic, Vanja Bortz, David M. |
| author_facet | Messenger, Daniel A. Tran, April Dukic, Vanja Bortz, David M. |
| contents | The weak form is a ubiquitous, well-studied, and widely-utilized mathematical tool in modern computational and applied mathematics. In this work we provide a survey of both the history and recent developments for several fields in which the weak form can play a critical role. In particular, we highlight several recent advances in weak form versions of equation learning, parameter estimation, and coarse graining, which offer surprising noise robustness, accuracy, and computational efficiency.
We note that this manuscript is a companion piece to our October 2024 SIAM News article of the same name. Here we provide more detailed explanations of mathematical developments as well as a more complete list of references. Lastly, we note that the software with which to reproduce the results in this manuscript is also available on our group's GitHub website https://github.com/MathBioCU . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06751 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Weak Form Is Stronger Than You Think Messenger, Daniel A. Tran, April Dukic, Vanja Bortz, David M. Machine Learning Computational Engineering, Finance, and Science Numerical Analysis 26A33, 35D30, 62FXX, 62JXX, 65L09, 65M32, 68Q32, The weak form is a ubiquitous, well-studied, and widely-utilized mathematical tool in modern computational and applied mathematics. In this work we provide a survey of both the history and recent developments for several fields in which the weak form can play a critical role. In particular, we highlight several recent advances in weak form versions of equation learning, parameter estimation, and coarse graining, which offer surprising noise robustness, accuracy, and computational efficiency. We note that this manuscript is a companion piece to our October 2024 SIAM News article of the same name. Here we provide more detailed explanations of mathematical developments as well as a more complete list of references. Lastly, we note that the software with which to reproduce the results in this manuscript is also available on our group's GitHub website https://github.com/MathBioCU . |
| title | The Weak Form Is Stronger Than You Think |
| topic | Machine Learning Computational Engineering, Finance, and Science Numerical Analysis 26A33, 35D30, 62FXX, 62JXX, 65L09, 65M32, 68Q32, |
| url | https://arxiv.org/abs/2409.06751 |