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<div id="divRplyFwdMsg" dir="ltr"><font style="font-size:11pt" face="Calibri, sans-serif" color="#000000"><b>From:</b> Monahan, Christopher <cjmonahan@wm.edu><br>
<b>Sent:</b> Wednesday, February 5, 2020 1:25 PM<br>
<b>To:</b> Mary Fox <mfox@jlab.org><br>
<b>Subject:</b> [EXTERNAL] FW: HEP/NP seminar Feb 13 - “Replacing Markov Chain Monte Carlo with Generative Flow Neural Networks” - Kimmy Cushman - Yale University</font>
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Hi Mary,</div>
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Please could you circulate this to the Theory Group? Thank you!</div>
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Regards,</div>
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Chris</div>
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<div id="divRplyFwdMsg" dir="ltr"><font style="font-size:11pt" face="Calibri, sans-serif" color="#000000"><b>From:</b> Wilkinson, Ellie V <evwilk@wm.edu><br>
<b>Sent:</b> 05 February 2020 11:48<br>
<b>To:</b> physics2017@physics.wm.edu <physics2017@physics.wm.edu><br>
<b>Subject:</b> HEP/NP seminar Feb 13 - “Replacing Markov Chain Monte Carlo with Generative Flow Neural Networks” - Kimmy Cushman - Yale University</font>
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<img alt="A person smiling for the camera
Description automatically generated" style="width:1.5312in; height:2.1979in" width="147" hspace="12" height="211" align="left" data-outlook-trace="F:2|T:2" src="cid:image003.jpg@01D5DC1A.3C1B2510"><b><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black">Speaker:
</span></b><span style="font-size:14.0pt; font-family:"Times New Roman",serif">Kimmy Cushman<span style="color:black">,
</span>Yale University</span></p>
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<b><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black">Title: “</span></b><i><span style="font-size:14.0pt; font-family:"Times New Roman",serif">Replacing Markov Chain Monte Carlo with Generative Flow Neural Networks<span style="color:black">”</span>
</span></i><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black"></span></p>
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<b><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black">Host:</span></b><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black"> </span><span style="font-size:14.0pt; font-family:"Times New Roman",serif">C.
Monahan<span style="color:black"></span></span></p>
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<b><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black">Time:</span></b><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black"> </span><span style="font-size:14.0pt; font-family:"Times New Roman",serif">3<span style="color:black">:</span>3<span style="color:black">0
pm </span>February<span style="color:black"> </span>13<span style="color:black">, 20</span>20<span style="color:black"></span></span></p>
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<b><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black">Place:</span></b><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black"> Physics Department,
</span><span style="font-size:14.0pt; font-family:"Times New Roman",serif">Small<span style="color:black"> 122</span></span></p>
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<span style="font-size:15.0pt; font-family:"Times New Roman",serif; color:black"> </span></p>
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<b><span style="font-size:15.0pt; font-family:"Times New Roman",serif"><br>
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</span></b><b><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black">Abstract:</span></b><span style="font-size:14.0pt; color:black"></span></p>
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<span style="font-size:14.0pt; font-family:"Times New Roman",serif">Quantum chromodynamics and other strongly coupled gauge theories are only solvable numerically, and the current state-of-the-art methods are variants of Markov Chain Monte Carlo (MCMC) integration
over particle fields defined on a discretized spacetime lattice. Properly sampling from the underlying distribution of lattice configurations is essential to computing correct observables, but traditional MCMC computational limitations place severe constraints
on the resolution of simulations which can be performed. In this talk, I will discuss progress made toward replacing Markov chain Monte Carlo approaches with generative flow neural networks for the generation of gauge configurations. I will explain the benefits
of using this architecture of neural network, and show our progress in implementing a one-dimensional spin theory. In this toy model, we implement a novel approach to confirming the physical accuracy of the ensembles by using a renormalization group Monte
Carlo method to verify that the configurations are in the same universality class as those produced by MCMC.</span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:#2F20A0">Cheers,</span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:#2F20A0"> </span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:#2F20A0">Ellie Wilkinson</span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:#2F20A0">William & Mary Physics</span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:#2F20A0">Administrative Coordinator</span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:#2F20A0">Small Hall 123</span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:#2F20A0">757-221-3503</span></p>
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