Mapping out phase diagrams with generative classifiers
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909205636579328 |
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| author | Arnold, Julian Schäfer, Frank Edelman, Alan Bruder, Christoph |
| author_facet | Arnold, Julian Schäfer, Frank Edelman, Alan Bruder, Christoph |
| contents | One of the central tasks in many-body physics is the determination of phase diagrams. However, mapping out a phase diagram generally requires a great deal of human intuition and understanding. To automate this process, one can frame it as a classification task. Typically, classification problems are tackled using discriminative classifiers that explicitly model the probability of the labels for a given sample. Here we show that phase-classification problems are naturally suitable to be solved using generative classifiers based on probabilistic models of the measurement statistics underlying the physical system. Such a generative approach benefits from modeling concepts native to the realm of statistical and quantum physics, as well as recent advances in machine learning. This leads to a powerful framework for the autonomous determination of phase diagrams with little to no human supervision that we showcase in applications to classical equilibrium systems and quantum ground states. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_14894 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Mapping out phase diagrams with generative classifiers Arnold, Julian Schäfer, Frank Edelman, Alan Bruder, Christoph Quantum Physics Disordered Systems and Neural Networks Computational Physics One of the central tasks in many-body physics is the determination of phase diagrams. However, mapping out a phase diagram generally requires a great deal of human intuition and understanding. To automate this process, one can frame it as a classification task. Typically, classification problems are tackled using discriminative classifiers that explicitly model the probability of the labels for a given sample. Here we show that phase-classification problems are naturally suitable to be solved using generative classifiers based on probabilistic models of the measurement statistics underlying the physical system. Such a generative approach benefits from modeling concepts native to the realm of statistical and quantum physics, as well as recent advances in machine learning. This leads to a powerful framework for the autonomous determination of phase diagrams with little to no human supervision that we showcase in applications to classical equilibrium systems and quantum ground states. |
| title | Mapping out phase diagrams with generative classifiers |
| topic | Quantum Physics Disordered Systems and Neural Networks Computational Physics |
| url | https://arxiv.org/abs/2306.14894 |