The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Alan S. Rodgers - One of the best experts on this subject based on the ideXlab platform.
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Conservation of angular momentum in RRKM theory. A three-Dimensional Model for nonlinear molecules
Chemical Physics Letters, 1994Co-Authors: Alan S. RodgersAbstract:Abstract The “usual” implementation of the unimolecular rate constant in RRKM theory treats nonlinear molecules as pseudo-diatomics and thus does not conserve total angular momentum. A three-Dimensional Model for conservation of total angular momentum for vibrator transition states in the K rotational quantum number either active or adiabatic is presented and a comparison made between this Model and the “usual” one in the low-pressure limit for two different reactions. This showed that the three-Dimensional Model resulted in significantly larger rate constants when the K rotor was active, indicating the importance of conservation of total angular momentum.
Erdoğan Şen - One of the best experts on this subject based on the ideXlab platform.
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CUBIC TRANSFORMATIONAL HIGH Dimensional Model REPRESENTATION
The Online Journal of Science and Technology, 2012Co-Authors: Erdoğan Şen, Kamil OruçoğluAbstract:This work focuses on the development of a multivariate function approximating method by using cubic Transformational High Dimensional Model Representation (THDMR). The method uses the target function’s image under a cubic transformation for High Dimensional Model Representation (HDMR) instead of the function’s itself.
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On Quartic Transformational High Dimensional Model Representation
2011Co-Authors: Erdoğan ŞenAbstract:This work focuses on the development of a multivariate function approximating method by using quartic Transformational High Dimensional Model Representation (THDMR). The method uses the target function’s image under a quartic transformation for High Dimensional Model Representation(HDMR) instead of the function’s itself.
Metin Demiralp - One of the best experts on this subject based on the ideXlab platform.
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A novel hybrid high Dimensional Model representation (HHDMR) based on the combination of plain and logarithmic high Dimensional Model representations
2007Co-Authors: Burcu Tunga, Metin DemiralpAbstract:This paper focuses on a new version of Hybrid High Dimensional Model Representation for multivariate functions. High Dimensional Model Representation (HDMR) was proposed to approximate the multivariate functions by the functions having less number of independent variables. Towards this end, HDMR disintegrates a multivariate function to components which are respectively constant, univariate, bivariate and so on in an ascending ordering of multivariance. HDMR method is a scheme truncating the representation at a prescribed multivariance. If the given multivariate function is purely additive then HDMR method spontaneously truncates at univariance, otherwise the highly multivariant terms are required. On the other hand, if the given function is dominantly multiplicative then the Logarithmic HDMR method which truncates the scheme at a prescribed multivariance of the HDMR of the logarithm of the given function is taken into consideration. In most cases the given multivariate function has both additive and multiplicative natures. If so then a new method is needed. Hybrid High Dimensional Model Representation method is used for these types of problems. This new representation method joins both plain High Dimensional Model Representation and Logarithmic High Dimensional Model Representation components via an hybridity parameter. This work focuses on the construction and certain details of this novel method.
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Logarithmic High Dimensional Model Representation
2006Co-Authors: Metin Demiralp, U Bilis, Urk IyeAbstract:This paper presents a new version of the High Dimensional Model Representation (HDMR) which attempts to approximate a given multivariate function by an expansion starting from a constant term and con- tinuing by adding univariate components and then the terms whose multivariances increase via bivariance, trivariance and so on. HDMR works well as long as the function under consideration behaves, more or less, ad- ditive. Factorized High Dimensional Model Representation (FHDMR) was considered as a powerful approach working well when the multivariate function under consideration is mostly multiplicative. The additivity of the function was dened through its HDMR components by introducing additivitiy measurers. FHDMR, un- fortunately, disabled us to dene e cient multiplicativity measurers. Hence, we develope Logarithmic High Dimensional Model Representation (LHDMR) to this end. It removes several unpleasent incapabilities of FHDMR.
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hybrid high Dimensional Model representation hhdmr on the partitioned data
Journal of Computational and Applied Mathematics, 2006Co-Authors: Alper M Tunga, Metin DemiralpAbstract:A multivariate interpolation problem is generally constructed for appropriate determination of a multivariate function whose values are given at a finite number of nodes of a multivariate grid. One way to construct the solution of this problem is to partition the given multivariate data into low-variate data. High Dimensional Model representation (HDMR) and generalized high Dimensional Model representation (GHDMR) methods are used to make this partitioning. Using the components of the HDMR or the GHDMR expansions the multivariate data can be partitioned. When a cartesian product set in the space of the independent variables is given, the HDMR expansion is used. On the other hand, if the nodes are the elements of a random discrete data the GHDMR expansion is used instead of HDMR. These two expansions work well for the multivariate data that have the additive nature. If the data have multiplicative nature then factorized high Dimensional Model representation (FHDMR) is used. But in most cases the nature of the given multivariate data and the sought multivariate function have neither additive nor multiplicative nature. They have a hybrid nature. So, a new method is developed to obtain better results and it is called hybrid high Dimensional Model representation (HHDMR). This new expansion includes both the HDMR (or GHDMR) and the FHDMR expansions through a hybridity parameter. In this work, the general structure of this hybrid expansion is given. It has tried to obtain the best value for the hybridity parameter. According to this value the analytical structure of the sought multivariate function can be determined via HHDMR.
Giovanna Vittori - One of the best experts on this subject based on the ideXlab platform.
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A three-Dimensional Model of sand bank formation
Ocean Dynamics, 2005Co-Authors: Giovanni Besio, Paolo Blondeaux, Giovanna VittoriAbstract:The results of a fully three-Dimensional Model for the generation of tidal sand banks are discussed. The Model is based on the linear stability analysis of the flat sea bed configuration subject to oscillatory tidal currents. The flow regime is assumed to be turbulent and a Boussinesq’s approach is adopted to Model Reynolds stresses.
Lionel Bertrand - One of the best experts on this subject based on the ideXlab platform.
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Analytical one-Dimensional Model to study the ultrasonic precursor generated by a laser
Physical Review E : Statistical Nonlinear and Soft Matter Physics, 1994Co-Authors: Marc Dubois, Franck Enguehard, Lionel BertrandAbstract:We present an analytical one-Dimensional Model of the laser generation of ultrasound that takes the optical penetration effect into account. This Model leads to a simple expression relating the full width at half maximum of the precursor to the product of the optical absorption coefficient by the longitudinal velocity, and establishes the conditions under which this expression is valid. The results of this Model are compared to those provided by a more sophisticated semianalytical three-Dimensional Model and to experimental data obteined under various experimental conditions. The agremment is very good.
