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The talk proposes a deep learning method specifically dealing with the forward and inverse problem of variable coefficient partial differential equations-Variable Coefficient Physics-Informed Neural N...
We extend the concept of self-consistency for the Fokker-Planck equation (FPE) [Shen et al., 2022] to the more general McKean-Vlasov equation (MVE). While FPE describes the macroscopic behavior of par...
This report consists of two parts associated with graph neural networks: generalization and graph structural learning. We first study the Rademacher complexity of GNNs, as one of independent-algorithm...
Along with the rapid development of artificial intelligence (AI) technology, scientific research enters a new era of AI. Topology optimization (TO) and AI technology are recently showing a growing tre...
The brain’s cerebral cortex produces perception based on the sensory information it’s fed through a region called the thalamus.
Deep neural networks, as a powerful system to represent high dimensional complex functions, play a key role in deep learning. Convergence of deep neural networks is a fundamental issue in building the...
One of neuroscience’s greatest challenges is to bridge the gaps between the external environment, the brain’s internal electrical activity, and the abstract workings of behavior and cognition. Many ne...
Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a c...
In this paper, we propose a spectral-spatial feature extraction framework based on deep learning (DL) for hyperspectral image (HSI) classification. In this framework, the variational autoencoder (VAE)...
The paper proposes a novel pansharpening method based on the pulse-coupled neural network segmentation. In the new method, uniform injection gains of each region are estimated through PCNN segmentatio...
Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectr...
The seismic damage information of buildings extracted from remote sensing (RS) imagery is meaningful for supporting relief and effective reduction of losses caused by earthquake. Both traditional pixe...
Global climate change, which mainly effected by human carbon emissions, would affect the regional economic, natural ecological environment, social development and food security in the near future. It’...
Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remot...
The classification of point clouds is the first step in the extraction of various types of geo-information form point clouds. Recently the ISPRS WG II/4 provides a benchmark on 3D semantic labelling, ...

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