Automatic early stopping using cross validation: quantifying the criteria. Adam: a method for stochastic optimization. 27th International Conference on Machine Learning 807–814 (2010). Rectified linear units improve restricted Boltzmann machines. Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows. Voronoi diagrams-a survey of a fundamental geometric data structure. First thesis on some properties of perfect positive quadratic forms. New applications of continuous parameters to the theory of quadratic forms. Deep learning for irregularly and regularly missing data reconstruction. Transfer learning with graph neural networks for short-term highway traffic forecasting. A comprehensive survey on graph neural networks. Learning representations by back-propagation errors. Super-resolution reconstruction of turbulent flows with machine learning. Gradient-based learning applied to document recognition. Aerodynamic Data Reconstruction and Inverse Design Using Proper Orthogonal Decomposition 42 (AIAA, 2004). Estimation of turbulent channel flow at R e τ = 100 based on the wall measurement using a simple sequential approach. Stochastic estimation of organized turbulent structure: homogeneous shear flow. ![]() A global stability analysis of the steady and periodic cylinder wake. Three-dimensional magnetic field reconstruction in the VKS experiment through Galerkin transforms. Assessment of supervised machine learning for fluid flows. In 2014 IEEE Geoscience and Remote Sensing Symposium 1832–1835 (IEEE, 2014).įukami, K., Fukagata, K. Compressed sensing applied to weather radar. A novel strategy for radar imaging based on compressive sensing. First M87 event horizon telescope results. Data-driven sparse sensor placement for reconstruction: demonstrating the benefits of exploiting known patterns. Our technique opens a new pathway toward the practical use of neural networks for real-time global field estimation. The current framework is able to handle an arbitrary number of moving sensors and thereby overcomes a major limitation with existing reconstruction methods. The proposed reconstruction technique is demonstrated for unsteady wake flow, geophysical data and three-dimensional turbulence. One of the central features of our method is its compatibility with deep learning-based super-resolution reconstruction techniques for structured sensor data that are established for image processing. In this work, we consider the use of Voronoi tessellation to obtain a structured-grid representation from sensor locations, enabling the computationally tractable use of convolutional neural networks. ![]() It should be noted that naive use of machine learning becomes prohibitively expensive for global field reconstruction and is furthermore not adaptable to an arbitrary number of sensors. As a solution to this problem, we propose a data-driven spatial field recovery technique founded on a structured grid-based deep-learning approach for arbitrary positioned sensors of any numbers. The key leverage in addressing this scientific issue is the wealth of data accumulated from the sensors. Moreover, these sensors could be in motion and could become online or offline over time. This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems. Contact us.Achieving accurate and robust global situational awareness of a complex time-evolving field from a limited number of sensors has been a long-standing challenge. You can see more tessellations from the world around us at How about you? Have you seen a tessellation in the world around you? Send us a photo or a link, and if it's really and truly a tessellation to talk about, we'll post it on this page with your name next to it. Tessellations Are All Around Us Have you seen a tessellation in the world around you? Photograph it, and contact us. How to Make an Asian Chop (stone stamp).
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