242 research outputs found
Relating probability distributions using geodesic least squares regression : application to edge-localized modes in fusion plasmas
No full textInternational Global H-Mode Confinement Database
No full textThis is the International Global H-Mode Confinement Database for tokamaks, version 5.2.3. A description of each variable, including selection variables, is available in the file DB5.2.3_variables.pdf.
A more detailed description of the database, as well as its analysis, is provided in the paper referenced below, and sources referenced therein.
Verdoolaege G, Kaye SM, Angioni C, Kardaun OJWF, Maslov M, Romanelli M, et al. "The updated ITPA global H-mode confinement database : description and analysis," Nuclear Fusion vol. 61, no. 7, art. no. 076006 (29pp), 2021, doi:10.1088/1741-4326/abdb91
Classification of ELM types in joint European Torus based on global plasma parameters using discriminant analysis
No full textIn this work, discriminant analysis is used as the main approach for building a physics based automated classifier for the discrimination of the edge-localized mode (ELM) plasma instability. The classifier is then applied for distinguishing type I and type III ELMs from a set of carbon-wall plasmas at JET. This provides a fast, standardized classification of ELM types which is expected to significantly reduce the effort of ELM experts in identifying ELM types. Further, the classifier yields a separation hyperplane in terms of global plasma parameters, which provides an insight into the range of conditions under which specific ELM behaviors occur. (C) 2017 Elsevier B.V. All rights reserved
Regression of fluctuating system properties : Baryonic Tully-Fisher scaling in disk galaxies
No full textGeodesic least squares regression for scaling studies in magnetic confinement fusion
No full textIn regression analyses for deriving scaling laws that occur in various scientific disciplines, usually standard regression methods have been applied, of which ordinary least squares (OLS) is the most popular. However, concerns have been raised with respect to several assumptions underlying OLS in its application to scaling laws. We here discuss a new regression method that is robust in the presence of significant uncertainty on both the data and the regression model. The method, which we call geodesic least squares regression (GLS), is based on minimization of the Rao geodesic distance on a probabilistic manifold. We demonstrate the superiority of the method using synthetic data and we present an application to the scaling law for the power threshold for the transition to the high confinement regime in magnetic confinement fusion devices
A new robust regression method based on minimization of geodesic distances on a probabilistic manifold: application to power laws
No full textIn regression analysis for deriving scaling laws that occur in various scientific disciplines, usually standard regression methods have been applied, of which ordinary least squares (OLS) is the most popular. In many situations, the assumptions underlying OLS are not fulfilled, and several other approaches have been proposed. However, most techniques address only part of the shortcomings of OLS. We here discuss a new and more general regression method, which we call geodesic least squares regression (GLS). The method is based on minimization of the Rao geodesic distance on a probabilistic manifold. For the case of a power law, we demonstrate the robustness of the method on synthetic data in the presence of significant uncertainty on both the data and the regression model. We then show good performance of the method in an application to a scaling law in magnetic confinement fusion
Geodesic least squares regression on information manifolds
No full textWe present a novel regression method targeted at situations with significant uncertainty on both the dependent and independent variables or with non-Gaussian distribution models. Unlike the classic regression model, the conditional distribution of the response variable suggested by the data need not be the same as the modeled distribution. Instead they are matched by minimizing the Rao geodesic distance between them. This yields a more flexible regression method that is less constrained by the assumptions imposed through the regression model. As an example, we demonstrate the improved resistance of our method against some flawed model assumptions and we apply this to scaling laws in magnetic confinement fusion
Geodesic least squares : robust regression using information geometry
No full textGeodesic least squares (GLS) is a regression technique that operates in spaces of probability 1
distributions. Based on minimization of the Rao geodesic distance between two probability models of 2
the response variable, GLS is robust against outliers and model misspecification. The method is very 3
simple, without any tuning parameters, owing to its solid foundations rooted in information geometry. 4
Here, we illustrate the robustness properties of GLS using applications in the fields of magnetic 5
confinement fusion and astrophysics. Additional interpretation is gained from visualizations using 6
several models for the manifold of Gaussian probability distributions
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