It's about time: a thermodynamic information criterion (TIC)
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908469885403136 |
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| author | Lucas, Brendan 05-06, Google Gemini 2. 5 Pro Preview |
| author_facet | Lucas, Brendan 05-06, Google Gemini 2. 5 Pro Preview |
| contents | Useful chemical processes often involve a desired steady state probability distribution, equilibrium or not, from which product is extracted. Given many different ways to attain the same steady state, which candidate "loses" the least in terms of time and energy? A scalar thermodynamic information criterion (TIC), inspired by AIC, assigns lower values to chemical processes with less estimated "loss" to generate the same desired steady state. As an element of thermodynamic machine learning, TIC naturally extends statistical objective optimization into the realm of chemical physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04519 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | It's about time: a thermodynamic information criterion (TIC) Lucas, Brendan 05-06, Google Gemini 2. 5 Pro Preview Statistical Mechanics Useful chemical processes often involve a desired steady state probability distribution, equilibrium or not, from which product is extracted. Given many different ways to attain the same steady state, which candidate "loses" the least in terms of time and energy? A scalar thermodynamic information criterion (TIC), inspired by AIC, assigns lower values to chemical processes with less estimated "loss" to generate the same desired steady state. As an element of thermodynamic machine learning, TIC naturally extends statistical objective optimization into the realm of chemical physics. |
| title | It's about time: a thermodynamic information criterion (TIC) |
| topic | Statistical Mechanics |
| url | https://arxiv.org/abs/2506.04519 |