The imperative of physics-based modeling and inverse theory in computational science
To best learn from data about large-scale complex systems, physics-based models representing the laws of nature must be integrated into the learning process. Inverse theory provides a crucial perspective for addressing the challenges of ill-posedness, uncertainty, nonlinearity and under-sampling.
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| Vydáno v: | Nature Computational Science Ročník 1; číslo 3; s. 166 - 168 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Nature Publishing Group
01.03.2021
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| ISSN: | 2662-8457, 2662-8457 |
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| Abstract | To best learn from data about large-scale complex systems, physics-based models representing the laws of nature must be integrated into the learning process. Inverse theory provides a crucial perspective for addressing the challenges of ill-posedness, uncertainty, nonlinearity and under-sampling. |
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| AbstractList | To best learn from data about large-scale complex systems, physics-based models representing the laws of nature must be integrated into the learning process. Inverse theory provides a crucial perspective for addressing the challenges of ill-posedness, uncertainty, nonlinearity and under-sampling. |
| Author | Willcox, Karen E. Heimbach, Patrick Ghattas, Omar |
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| Title | The imperative of physics-based modeling and inverse theory in computational science |
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