



@article {2466,
	title = {Machine learning the thermodynamic arrow of time},
	journal = {Nat. Phys.},
	year = {2020},
	month = {09/21/2020},
	pages = {1-9},
	abstract = {<p><span style="color: rgb(51, 51, 51); font-family: \&quot;Open Sans\&quot;, \&quot;Lucida Grande\&quot;, \&quot;Helvetica Neue\&quot;, Helvetica, Arial, sans-serif; font-size: 14px;">The mechanism by which thermodynamics sets the direction of time\&$\#$39;s arrow has long fascinated scientists. Here, we show that a machine learning algorithm can learn to discern the direction of time\&$\#$39;s arrow when provided with a system\&$\#$39;s microscopic trajectory as input. The performance of our algorithm matches fundamental bounds predicted by nonequilibrium statistical mechanics. Examination of the algorithm\&$\#$39;s decision-making process reveals that it discovers the underlying thermodynamic mechanism and the relevant physical observables. Our results indicate that machine learning techniques can be used to study systems out of equilibrium, and ultimately to uncover physical principles.</span></p>},
	doi = {https://doi.org/10.1038/s41567-020-1018-2},
	url = {https://arxiv.org/abs/1909.12380},
	author = {Alireza Seif and Mohammad Hafezi and Christopher Jarzynski}
}
