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arXiv · 2109.08497

Interpreting machine learning functions as physical observables

Gert Aarts·Dimitrios Bachtis·Biagio Lucini
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Abstract

We propose to interpret machine learning functions as physical observables, opening up the possibility to apply "standard" statistical-mechanical methods to outputs from neural networks. This includes histogram reweighting and finite-size scaling, to analyse phase transitions quantitatively. In addition we incorporate predictive functions as conjugate variables coupled to an external field within the Hamiltonian of a system, allowing to induce order-disorder phase transitions in a novel manner. A noteworthy feature of this approach is that no knowledge of the symmetries in the Hamiltonian is required.

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Gert Aarts, Dimitrios Bachtis, Biagio Lucini. 2021-09-17. Interpreting machine learning functions as physical observables. https://arxiv.org/abs/2109.08497

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