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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| Veröffentlicht in: | Nature Computational Science Jg. 1; H. 3; S. 166 - 168 |
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01.03.2021
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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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