Overcoming Data Scarcity in Deep Learning of Scientific Problems
Data-driven approaches such as machine learning have been increasingly applied to the natural sciences, e.g. for property prediction and optimization or material discovery. An essential criteria to ensure the success of such methods is the need for extensive amounts of labeled data, making it unfeas...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/140165 |