83 citations · 278 across the 9 of their papers we have counts for
9 papers
DeepVATS: Deep Visual Analytics for Time Series
Victor Rodriguez-Fernandez, David Montalvo, Francesco Piccialli +2
The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to provide them with large-…
A Deep Learning Approach for Predicting Two-dimensional Soil Consolidation Using Physics-Informed Neural Networks (PINN)
Yue Lu, Gang Mei, Francesco Piccialli
Soil consolidation is closely related to seepage, stability, and settlement of geotechnical buildings and foundations, and directly affects the use and safety of superstructures. N…
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
Salvatore Cuomo, Vincenzo Schiano di Cola, Fabio Giampaolo +3
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itsel…
Deep Learning for the Approximation of a Shape Functional
F. Calabrò, S. Cuomo, F. Giampaolo +4
Artificial Neuronal Networks are models widely used for many scientific tasks. One of the well-known field of application is the approximation of high-dimensional problems via Deep…
An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving
Jingzhi Tu, Gang Mei, Francesco Piccialli
Autonomous driving is the key technology of intelligent logistics in Industrial Internet of Things (IIoT). In autonomous driving, the appearance of incomplete point clouds losing g…
Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Prediction
Zhengjing Ma, Gang Mei, Salvatore Cuomo +1
Heterogeneous data are commonly adopted as the inputs for some models that predict the future trends of some observations. Existing predictive models typically ignore the inconsist…
KCoreMotif: An Efficient Graph Clustering Algorithm for Large Networks by Exploiting k-core Decomposition and Motifs
Gang Mei, Jingzhi Tu, Lei Xiao +1
Clustering analysis has been widely used in trust evaluation on various complex networks such as wireless sensors networks and online social networks. Spectral clustering is one of…
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues
Kaifeng Gao, Gang Mei, Francesco Piccialli +3
Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning…
Adaptive RBF Interpolation for Estimating Missing Values in Geographical Data
Kaifeng Gao, Gang Mei, Salvatore Cuomo +2
The quality of datasets is a critical issue in big data mining. More interesting things could be mined from datasets with higher quality. The existence of missing values in geograp…