<story><title>A neural net solves the three-body problem 100M times faster</title><url>https://www.technologyreview.com/s/614597/a-neural-net-solves-the-three-body-problem-100-million-times-faster/</url></story><parent_chain><item><author>conistonwater</author><text>If I understood correctly, on page 2 they say they generated their training and test cases using random initial positions and velocities for the 3-body problem. But this is exactly the sort of case that a non-sophisticated solver should be able to handle easily as well. Brutus is special in the sense that it tries hard to give some sort of a guarantee about the accuracy of its solution to a chaotic problem. But the neural network solver provides no such guarantee, and has not been trained or evaluated on &amp;quot;hard&amp;quot; examples. I&amp;#x27;m not sure the comparison is fair or particularly meaningful. I wonder if exactly the same sort of &amp;quot;positive&amp;quot; result could be achieved with a standard symplectic integrator, it&amp;#x27;s not at all obvious from the paper that they have ruled that out.</text></item></parent_chain><comment><author>knzhou</author><text>Yeah, this is classic neural network hype. I wish there were a way to automatically copy-paste my usual comment on trusting the MIT Tech Review, and &lt;i&gt;especially&lt;/i&gt; its Emerging Technologies column, whenever such a link is submitted.</text></comment>
<story><title>A neural net solves the three-body problem 100M times faster</title><url>https://www.technologyreview.com/s/614597/a-neural-net-solves-the-three-body-problem-100-million-times-faster/</url></story><parent_chain><item><author>conistonwater</author><text>If I understood correctly, on page 2 they say they generated their training and test cases using random initial positions and velocities for the 3-body problem. But this is exactly the sort of case that a non-sophisticated solver should be able to handle easily as well. Brutus is special in the sense that it tries hard to give some sort of a guarantee about the accuracy of its solution to a chaotic problem. But the neural network solver provides no such guarantee, and has not been trained or evaluated on &amp;quot;hard&amp;quot; examples. I&amp;#x27;m not sure the comparison is fair or particularly meaningful. I wonder if exactly the same sort of &amp;quot;positive&amp;quot; result could be achieved with a standard symplectic integrator, it&amp;#x27;s not at all obvious from the paper that they have ruled that out.</text></item></parent_chain><comment><author>pixelpoet</author><text>Argh, just looked up Brutus and it&amp;#x27;s pretty much &lt;i&gt;exactly&lt;/i&gt; what I wanted to do on my own after quite a lot of reading on n-body simulations and finding nothing like it out there. I was (very naively, I realise) hoping to do some original research.&lt;p&gt;Oh well, time to study this intensively. Here&amp;#x27;s a link for the curious: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;abs&amp;#x2F;1411.6671&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;abs&amp;#x2F;1411.6671&lt;/a&gt;</text></comment>