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Machine learning and the physical sciences
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In October 2018 an APS Physics Next Workshop on Machine Learning was held in Riverhead, NY. This article reviews and summarizes the proceedings of this very broad, emerging field.This needs to be a placard in the left-hand column, with a custom tag.
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“A review on the manifold recent research topics in the physical sciences is given by [109], with applications in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics.”
BackgroundThe frontier of simulation-based inference
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Self-citation
Metadata
- Authors
- Carleo, G., Cirac, I., Cranmer, K., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L.
- Venue
- Reviews of Modern Physics
- Publisher
- American Physical Society (APS)
- Published
- 2019-12-06
- Volume
- 91
- Issue
- 4
- ISSN
- 0034-6861 (ISSN-L)
- 1539-0756
- OpenAlex
- W2923537029
- Cited by
- 2,417
- References
- 53
- FWCI
- 89.01
- Type
- journal article
- Language
- en
- Open access
- green
- Funders
- National Science Foundation (PHY-1748958, ACI-1450310, OAC-1836650, DMR-1420073)
- Army Research Office (W911NF-13-1-0387)
- H2020 European Research Council (714608-SMiLe)
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- Carleo, G., Cirac, I., Cranmer, K., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L. (2019). Machine learning and the physical sciences. Reviews of Modern Physics. https://doi.org/10.1103/revmodphys.91.045002
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