Extracting Interpretable Physical Parameters from Spatiotemporal Systems Using Unsupervised Learning
© 2020 authors. Experimental data are often affected by uncontrolled variables that make analysis and interpretation difficult. For spatiotemporal systems, this problem is further exacerbated by their intricate dynamics. Modern machine learning methods are particularly well suited for analyzing and...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Physical Society (APS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/134435.2 |