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Journal of Scientific Computing, 2023
In this paper, based on the combination of tensor neural network and a posteriori error estimator, a novel type of machine learning method is proposed to solve high-dimensional boundary value problems with homogeneous and non-homogeneous Dirichlet or ...
Yifan Wang +4 more
semanticscholar +1 more source
In this paper, based on the combination of tensor neural network and a posteriori error estimator, a novel type of machine learning method is proposed to solve high-dimensional boundary value problems with homogeneous and non-homogeneous Dirichlet or ...
Yifan Wang +4 more
semanticscholar +1 more source
, 2020
Uncertainty complicates the optimization model of distributed energy systems, it is a challenge to address the fragility of optimal solutions, which calls for an effective but convenient approach to introduce uncertainties into multi-objective ...
Meng Wang +5 more
semanticscholar +1 more source
Uncertainty complicates the optimization model of distributed energy systems, it is a challenge to address the fragility of optimal solutions, which calls for an effective but convenient approach to introduce uncertainties into multi-objective ...
Meng Wang +5 more
semanticscholar +1 more source
A Posteriori Error Estimates for the Weak Galerkin Finite Element Methods on Polytopal Meshes
Communications in Computational Physics, 2019In this paper, we present a simple a posteriori error estimate for the weak Galerkin (WG) finite element method for a model second order elliptic equation.
Hengguang Li
semanticscholar +1 more source
A Posteriori Hyperspectral Anomaly Detection for Unlabeled Classification
IEEE Transactions on Geoscience and Remote Sensing, 2018Anomaly detection (AD) generally finds targets that are spectrally distinct from their surrounding neighborhoods but cannot discriminate its detected targets one from another.
Yulei Wang +7 more
semanticscholar +1 more source

