Journal ArticleDOI
2006 Special issue: Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction
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A new type of numerical model, a complex hybrid environmental model based on a synergetic combination of deterministic and machine learning model components, has been introduced in this paper for applications to climate modeling and weather prediction.About:
This article is published in Neural Networks.The article was published on 2006-03-01. It has received 147 citations till now. The article focuses on the topics: Artificial neural network.read more
Citations
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Journal ArticleDOI
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective
TL;DR: This work reviews the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.
Posted Content
Integrating Physics-Based Modeling with Machine Learning: A Survey
TL;DR: An overview of techniques to integrate machine learning with physics-based modeling and classes of methodologies used to construct physics-guided machine learning models and hybrid physics-machine learning frameworks from a machine learning standpoint is provided.
Proceedings ArticleDOI
A Deep Hybrid Model for Weather Forecasting
TL;DR: This work studies specifically the power of making predictions via a hybrid approach that combines discriminatively trained predictive models with a deep neural network that models the joint statistics of a set of weather-related variables.
Journal ArticleDOI
Monitoring of wind farms’ power curves using machine learning techniques
TL;DR: In this article, a data-driven approach for building an equivalent steady state model of a wind farm under normal operating conditions is presented, which can be used for the creation of quality control charts at the aim of detecting anomalous functioning conditions of the wind farm.
Book ChapterDOI
Data Assimilation for Numerical Weather Prediction: A Review
TL;DR: Data assimilation has gradually reached a mature center stage position at both Numerical Weather Prediction centers as well as being at the center of activities at many federal research institutes and at many universities as mentioned in this paper.
References
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Journal ArticleDOI
Approximation by superpositions of a sigmoidal function
TL;DR: It is demonstrated that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube.
Journal ArticleDOI
Approximation capabilities of multilayer feedforward networks
TL;DR: It is shown that standard multilayer feedforward networks with as few as a single hidden layer and arbitrary bounded and nonconstant activation function are universal approximators with respect to L p (μ) performance criteria, for arbitrary finite input environment measures μ.
Journal ArticleDOI
On the approximate realization of continuous mappings by neural networks
TL;DR: It is proved that any continuous mapping can be approximately realized by Rumelhart-Hinton-Williams' multilayer neural networks with at least one hidden layer whose output functions are sigmoid functions.
Proceedings ArticleDOI
Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights
D. Nguyen,Bernard Widrow +1 more
TL;DR: The authors describe how a two-layer neural network can approximate any nonlinear function by forming a union of piecewise linear segments and a method is given for picking initial weights for the network to decrease training time.
A Thermal Infrared Radiation Parameterization for Atmospheric Studies
TL;DR: In this article, the authors developed a longwave radiation parameterization for a wide variety of weather and climate applications based on the 1996-version of the Air Force Geophysical Laboratory HITRAN data.