



01316nas a2200145   4500008004100000245005300041210005300094260001500147300000800162520089700170100001801067700002101085700002701106856003701133       2020                        eng d00aMachine learning the thermodynamic arrow of time0 aMachine learning the thermodynamic arrow of time  c09/21/2020  a1-93 a<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>1 aSeif, Alireza1 aHafezi, Mohammad1 aJarzynski, Christopher  uhttps://arxiv.org/abs/1909.12380