Academics

A Neural Networks Solver for R Matrices

Time:2023-05-29 Mon 10:00-12:00

Venue:Venue: A3-2a-201 ZOOM:819 4969 3781(PW: BIMSA)

Organizer:Hongfei Shu, Hao Zou, Ruidong Zhu

Speaker:Shailesh Lal BIMSA

Abstract

we describe how neural networks may be used to learn solutions to the Yang-Baxter Equation using 2d spin chains of difference form as a concrete example. the talk is based on arxiv:2304.07247, which is joint work with Suvajit Majumder and Evgeny Skvortsov.


Speaker Intro

Dr Shailesh Lal received his PhD from the Harish-Chandra Research Institute. His research interests are applications of machine learning to string theory and mathematical physics, black holes in string theory and higher-spin holography.

DATEMay 29, 2023
SHARE
Related News
    • 0

      Mean-field theory of learning dynamics in deep neural networks

      AbstractLearning dynamics of deep neural networks is complex. While previous approaches made advances in mathematical analysis of the dynamics of two-layer neural networks, addressing deeper networks have been challenging. In this talk, I will present a mean field theory of the learning dynamics of deep networks and discuss its practical implications

    • 1

      Learning constitutive models with neural networks

      AbstractIn this talk, I will introduce some work of learning constitutive equations in fluid mechanics and geophysics based on machine learningSpeaker Intro熊繁升,现任北京雁栖湖应用数学研究院助理研究员,曾任北京应用物理与计算数学研究所所聘博士后。先后毕业于中国地质大学(北京)、清华大学,美国耶鲁大学联合培养博士。研究兴趣主要集中于基于机器学习算法(DNN、PINN、DeepONet等)求解微分方程模型正/反问题...