https://OkongOyangO.github.io/OkongOyangO.JournalClub/tags/neural-networks/Recent content in Neural Networks on Condensed Matter🍩 + AI🤖 Journal Club🎓Hugoenyzj5306@psu.edu (Yiyang Jiang)yzj5306@psu.edu (Yiyang Jiang)Mon, 03 Aug 2026 16:30:00 -0400https://OkongOyangO.github.io/OkongOyangO.JournalClub/posts/2026-08-neural-networks-for-physicists/Mon, 03 Aug 2026 16:30:00 -0400yzj5306@psu.edu (Yiyang Jiang)https://OkongOyangO.github.io/OkongOyangO.JournalClub/posts/2026-08-neural-networks-for-physicists/<table> <thead> <tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td><strong>Presenter</strong></td> <td>Mu-Yang Chen (Prof. Chao-Xing Liu&rsquo;s group, Penn State)</td> </tr> <tr> <td><strong>Date</strong></td> <td>August 3, 2026 · 4:30–6:00 PM</td> </tr> <tr> <td><strong>Location</strong></td> <td>Davey 339</td> </tr> <tr> <td><strong>Topic</strong></td> <td>Neural networks for physicists — from one neuron to attention</td> </tr> </tbody> </table> <p>A three-part pedagogical tour: what a neural network actually is and how it is trained, what attention adds once the data are sequences, and how both are being used right now in many-body physics — neural-network wavefunction ansätze optimised by variational Monte Carlo, and reduced density matrices learned without the wavefunction at all. The organising claim: a network is a very flexible fitting function, and the physics lives entirely in the details of how much of it you build in by hand.</p>