How Neural Networks reduce the Fermionic Sign Problem and what we can learn from them
- 日時
- 2024年12月11日(水)15:30 - 16:30 (JST)
- 講演者
-
- Johann Ostmeyer (Post-doctoral Fellow, Helmholtz-Institut für Strahlen- und Kernphysik, University of Bonn, Germany)
- 会場
- 言語
- 英語
- ホスト
- Enrico Rinaldi
When simulating fermionic quantum systems, non-perturbative Monte Carlo techniques are often the most efficient approach known to date. However, beyond half filling they suffer from the so-called sign problem, i.e. negative "probabilities", so that stochastic sampling becomes infeasible. Recently, considerable progress has been made in alleviating the sign problem by deforming the integration contour of the path integral into the complex plane and applying machine learning to find near-optimal alternative contours. In this talk, I am going to present a particularly successful architecture, based on complex-valued affine coupling layers. Furthermore, I will demonstrate how insight gained from the trained network can be used for simpler analytic approaches.
このイベントは研究者向けのクローズドイベントです。一般の方はご参加頂けません。メンバーや関係者以外の方で参加ご希望の方は、フォームよりお問い合わせ下さい。講演者やホストの意向により、ご参加頂けない場合もありますので、ご了承下さい。