no code implementations • 17 Apr 2023 • Jiaxu Liu, Song Chen, Shengze Cai, Chao Xu
In this paper, we investigate a distributed aggregative optimization problem in a network, where each agent has its own local cost function which depends not only on the local state variable but also on an aggregated function of state variables from all agents.
no code implementations • 8 Mar 2023 • Jiaxu Liu, Song Chen, Shengze Cai, Chao Xu
The vanilla fractional order gradient descent may oscillatively converge to a region around the global minimum instead of converging to the exact minimum point, or even diverge, in the case where the objective function is strongly convex.
no code implementations • 31 Oct 2022 • Jiaming Liang, Chao Xu, Shengze Cai
By introducing a novel deep neural network based on recurrent Graph Optimal Transport, called GotFlow3D, we present an end-to-end solution to learn the 3D fluid flow motion from double-frame particle sets.
no code implementations • 27 Aug 2022 • Song Chen, Shengze Cai, Tehuan Chen, Chao Xu, Jian Chu
In this paper, we propose a novel nonlinear observer based on neural networks, called neural observer, for observation tasks of linear time-invariant (LTI) systems and uncertain nonlinear systems.
no code implementations • 5 Jun 2021 • Qian Zhang, Konstantina Sampani, Mengjia Xu, Shengze Cai, Yixiang Deng, He Li, Jennifer K. Sun, George Em Karniadakis
Microaneurysms (MAs) are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness.
no code implementations • 20 May 2021 • Shengze Cai, Zhiping Mao, Zhicheng Wang, Minglang Yin, George Em Karniadakis
Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier-Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is complex, and we cannot tackle high-dimensional problems governed by parametrized NSE.
no code implementations • 23 Dec 2020 • Chensen Lin, Zhen Li, Lu Lu, Shengze Cai, Martin Maxey, George Em Karniadakis
Simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs).
Computational Physics