Search Results for author: Shengze Cai

Found 7 papers, 0 papers with code

Accelerated Distributed Aggregative Optimization

no code implementations17 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.

The Novel Adaptive Fractional Order Gradient Decent Algorithms Design via Robust Control

no code implementations8 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.

GotFlow3D: Recurrent Graph Optimal Transport for Learning 3D Flow Motion in Particle Tracking

no code implementations31 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.

Motion Estimation

Neural Observer with Lyapunov Stability Guarantee for Uncertain Nonlinear Systems

no code implementations27 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.

Physics-informed neural networks (PINNs) for fluid mechanics: A review

no code implementations20 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.

Operator learning for predicting multiscale bubble growth dynamics

no code implementations23 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

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