121 citations · 148 across the 5 of their papers we have counts for
5 papers
Does Lorentz-symmetric design boost network performance in jet physics?
Congqiao Li, Huilin Qu, Sitian Qian +5
In the deep learning era, improving the neural network performance in jet physics is a rewarding task as it directly contributes to more accurate physics measurements at the LHC. R…
Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs
Peiyan Hu, Qi Meng, Bingguang Chen +6
Stochastic partial differential equations (SPDEs) are significant tools for modeling dynamics in many areas including atmospheric sciences and physics. Neural Operators, generation…
An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
Shiqi Gong, Qi Meng, Jue Zhang +6
Deep learning methods have been increasingly adopted to study jets in particle physics. Since symmetry-preserving behavior has been shown to be an important factor for improving th…
Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics
Shiqi Gong, Qi Meng, Yue Wang +4
Learning dynamics governed by differential equations is crucial for predicting and controlling the systems in science and engineering. Neural Ordinary Differential Equation (NODE),…
Dynamic of Stochastic Gradient Descent with State-Dependent Noise
Qi Meng, Shiqi Gong, Wei Chen +2
Stochastic gradient descent (SGD) and its variants are mainstream methods to train deep neural networks. Since neural networks are non-convex, more and more works study the dynamic…