Nonlinear thermodynamic computing out of equilibrium
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914236929671168 |
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| author | Whitelam, Stephen Casert, Corneel |
| author_facet | Whitelam, Stephen Casert, Corneel |
| contents | We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarded as thermodynamic neurons, and can serve as the building blocks of networked structures that act as thermodynamic neural networks, universal function approximators whose operation is powered by thermal fluctuations. We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium. This work expands the field of thermodynamic computing beyond the regime of thermal equilibrium, enabling fully nonlinear computations, analogous to those performed by classical neural networks, at specified observation times. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17183 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nonlinear thermodynamic computing out of equilibrium Whitelam, Stephen Casert, Corneel Statistical Mechanics Machine Learning Neural and Evolutionary Computing We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarded as thermodynamic neurons, and can serve as the building blocks of networked structures that act as thermodynamic neural networks, universal function approximators whose operation is powered by thermal fluctuations. We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium. This work expands the field of thermodynamic computing beyond the regime of thermal equilibrium, enabling fully nonlinear computations, analogous to those performed by classical neural networks, at specified observation times. |
| title | Nonlinear thermodynamic computing out of equilibrium |
| topic | Statistical Mechanics Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2412.17183 |