PPT-1 Discretization

of Fluid Models Navier Stokes Dr Farzad Ismail School of Aerospace and Mechanical Engineering Universiti Sains Malaysia Nibong Tebal 14300 Pulau Pinang Week 5 Lecture 1 and 2 2. Consistency The discretization of a PDE should become exac t as the mesh size tends to zero truncation error should vanish 2 Stability Numerical errors which are generated during the solution of discretized equations should not be magni64257ed 3 Con Webb School of Computing and Mathematics Deakin University VIC 3125 Australia yyangdeakineduau School of Computer Science and Software Engineering Monash University VIC 3800 Australia Geo64256Webbmailcssemonasheduau Abstract Discretization is a popu e presen t a comparison of three en trop ybased discretiza tion metho ds in a con text of learning classi cation rules W e compare the binary recursiv e discretization with a stopping criterion based on the Minim um Description Length Principle MDLP Rony. . Goldenthal. et al. . Presenter : . SoHyeon. . Jeong. . (SoHyeon.Jeong@gmail.com). ACM SIGGRAPH 2007. Movie. Contents. Abstract. Introduction. Related Work. Cloth Model. Constrained Dynamics. Analysis. Santiago González. <sgonzalez@fi.upm.es>. Contents. Introduction. . CRISP-DM (1). Tools . Data . understanding. Data . preparation. Modeling. (2). Association. rules. ? . Supervised. Case summary. First International High-Order . CFD Workshop. Jan. 7-8, 2012, Nashville, TN. Doru Caraeni, CD-Adapco.. Case 1.6 Vortex transport by uniform flow. Canonical test case to:. Assess efficiency of HO methods for LES/DES of turbulent flows,. of Fluid Models. (. Navier. Stokes). Dr. Farzad Ismail. School of Aerospace and Mechanical Engineering. Universiti Sains Malaysia. Nibong Tebal 14300 Pulau Pinang. Week . 5 . (Lecture 1 and 2). 2. Preview. Data preprocessing Discretization of continuous data Attribute reduction Model establishing of neural network 205 0.2, then If 0.2 0.4, then = 2; If 0.4 0.2, then = 3; If 0.6 0.2, t Daisuke . Hotta. hotta@umd.edu. Advisor: Prof. Eugenia . Kalnay. Dept. of Atmospheric and Oceanic Science, . University of Maryland, College Park. ekalnay@atmos.umd.edu. Numerical Weather Prediction (NWP):. Kazhdan. [. Taubin. , 1995] . A Signal Processing Approach to Fair Surface . Design. [. Desbrun. , . et al.. , 1999] Implicit Fairing of Arbitrary Meshes…. [. Vallet. and Levy, 2008] . Spectral Geometry Processing with Manifold . Author: Daisuke . Hotta. hotta@umd.edu. Advisor: Prof. Eugenia . Kalnay. Dept. of Atmospheric and Oceanic Science, . University of Maryland, College Park. ekalnay@atmos.umd.edu. Numerical Weather Prediction (NWP):. . Ras. X. a. b. d. x1. 0.8. 2. 1. x2. 1. 0.5. 0. x3. 1.3. 3. 0. x4. 1.4. 1. 1. x5. 1.4. 2. 0. x6. 1.6. 3. 1. x7. 1.3. 1. 1. Quantization Process (based on . discernibility. formulas). V. a. = [0, 2) .  . Ismail B. Celik. Mechanical and Aerospace Engineering Department. West Virginia University (. WVU. ), Morgantown WV 26506. ibcelik@mail.wvu.edu. Contributors:. . Zhiyuan . Ma* Sofiane Benyahia**, . What Is Data Mining?. Many people treat data mining as a synonym for another popularly used term, knowledge discovery from data, or KDD, while others view data mining as merely an essential step in the process of knowledge discovery. .