PPT-(Some) Open Issues in Nuclear modeling for (General Purpose) (Monte Carlo) Numerical Simulations

AFerrari Catania November 2012 CERN Geneva helpsuggestions from several people 14112012 Alfredo Ferrari Catania 2 Mark Chadwick and Toshihiko Kawano Gnash MCNP LANL Sylvie Leray. G.S. Karlovits, J.C. Adam, Washington State University. 2010 AGU Fall Meeting, San Francisco, CA. Outline. Climate change and uncertainty in the Pacific Northwest. Data, model and methods. Climate data. Pete . Truscott. 1. , . Daniel . Heynderickx. 2. , . Fan . Lei. 3. , . Athina . Varotsou. 4. , . Piers . Jiggens. 5. . and Alain . Hilgers. 5. (1) Kallisto Consultancy , UK; (2) DH Consultancy, Belgium; (3) . Steven . Gollmer. Cedarville University. Meet and Greet Game. Are there people here who share the same birthday?. Most births occur in September & October. October 5. th. is the most common birthday. . + Monte-Carlo techniques. Michael Ireland (RSAA. ). The key to Bayesian probability is Bayes’ theorem, which can be written: . Derived in any good textbook, D can be any event, but is written as D because it is typically a particular set of data.. Basic Principles and Recent Progress. Most slides by. Alan . Fern. EECS, Oregon . State . University. A few from me, Dan Klein, Luke . Zettlmoyer. , etc . Dan Weld – UW CSE 573. October 2012. (Monaco). Monte Carlo Timeline. 10 June 1215. Monaco is taken by the Genoese. 1489. The King of France, Charles VIII, and the Duke of Savoy recognize the sovereignty of Monaco . 1512. Louis XII, King of France, recognizes the independence of Monaco. SIMULATION. Simulation . of a process . – the examination . of any emulating process simpler than that under consideration. .. Examples:. System’s Simulation such as simulation of engineering systems, large organizational systems, and governmental systems. Simple Monte Carlo . Integration. Suppose . that we pick N random points, . uniformly . distributed in a . multidimensional volume . V . Call . them x. 0. ,… . ; . x. N-1. . Then the basic theorem of Monte . Imry. Rosenbaum. Jeremy . Staum. Outline. What is simulation . metamodeling. ?. Metamodeling. approaches. Why use function approximation?. Multilevel Monte Carlo. MLMC in . metamodeling. Simulation . Leiming Yu, Fanny Nina-Paravecino, David Kaeli, Qianqian Fang. 1. Outline. Monte Carlo . eXtreme. GPU Computing. MCX. in OpenCL. Conclusion. 2. Monte Carlo . eXtreme. Estimates the 3D light (. fluence. Monte Carlo In A Nutshell. Using a large number of simulated trials in order to approximate a solution to a problem. Generating random numbers. Computer not required, though extremely helpful . A Brief History. 19: High Quality Rendering . Ravi . Ramamoorthi. http:/. /viscomp.ucsd.edu/classes/cse167/wi17. Summary. This is the final lecture of CSE 167. Good luck on HW 4, written assignment. Please consider CSE 163 (mine), CSE 168 spring. 1WATER CLUSTERSZSZidi a SV Schevkunov ba Physics and Chemistry Dept Gabes preparatory institute ofengineers studies Rue OMAR IBNU ELKATTAB ZRIG GABES 6029Tunisiae-mail zidizblackcodemailcomb Physics a In of our series, where in the past we have discussed the ( i ) Black Scholes model and the (ii) Binomial option pricing model, we present the Monto Carlo simulation model to conclude our series on op