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 Preview. 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 Some Machine Learning algorithms require a discrete feature space but in realworld applications con tinuous attributes must be handled To deal with this problem many supervised discretization meth ods have been proposed but little has been done to s 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 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 Data Preparation & Preprocessing. Bamshad Mobasher. DePaul University. 2. The Knowledge Discovery Process. - The KDD Process. 3. Data Preprocessing. Why do we need to prepare the data?. In real world applications data can be . are given on discretization yields!!f!t+U!f!x=0fjn+1=fjn!"t2hU(fj+1n!fj!1n)j-1 j j+1n+1!Methods for Advection!Computational Fluid Dynamics!This scheme is O($t, h2) accurate, but a stabi Jan Martin Nordbotten. Department of Mathematics, University of Bergen, Norway. Department of Civil and Environmental Engineering, Princeton University, USA. VISTA – Norwegian Academy of Sciences and Letters and Statoil ASA. Hydrodynamics. Fall 2011 Review. PDT and radiation transport. Marvin L. Adams. We continue to develop and PDT . and apply it to CRASH problems.. Assessing diffusion model error is a CRASH priority.. Eric Myra will discuss . 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 . Data Preparation & Preprocessing. Bamshad Mobasher. DePaul University. 2. The Knowledge Discovery Process. - The KDD Process. 3. Data Preprocessing. Why do we need to prepare the data?. In real world applications data can be . . 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**, . Case . of Santa Olalla pond (SW . Spain. ). . Serrano-Hidalgo, C., Heredia-Díaz J. ., Guardiola-Albert C. . and . Elorza-Tenreiro. F.J.. METODOLOGY. T. o identify the adequate numerical representation of the lagoon using a finer spatial discretization..