Neural Mode Jump Monte Carlo
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2019
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| _version_ | 1866910460634202112 |
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| author | Sbailò, Luigi Dibak, Manuel Noé, Frank |
| author_facet | Sbailò, Luigi Dibak, Manuel Noé, Frank |
| contents | Markov chain Monte Carlo methods are a powerful tool for sampling equilibrium configurations in complex systems. One problem these methods often face is slow convergence over large energy barriers. In this work, we propose a novel method which increases convergence in systems composed of many metastable states. This method aims to connect metastable regions directly using generative neural networks in order to propose new configurations in the Markov chain and optimizes the acceptance probability of large jumps between modes in configuration space. We provide a comprehensive theory and demonstrate the method on example systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1912_05216 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Neural Mode Jump Monte Carlo Sbailò, Luigi Dibak, Manuel Noé, Frank Computational Physics Biological Physics Markov chain Monte Carlo methods are a powerful tool for sampling equilibrium configurations in complex systems. One problem these methods often face is slow convergence over large energy barriers. In this work, we propose a novel method which increases convergence in systems composed of many metastable states. This method aims to connect metastable regions directly using generative neural networks in order to propose new configurations in the Markov chain and optimizes the acceptance probability of large jumps between modes in configuration space. We provide a comprehensive theory and demonstrate the method on example systems. |
| title | Neural Mode Jump Monte Carlo |
| topic | Computational Physics Biological Physics |
| url | https://arxiv.org/abs/1912.05216 |