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Advanced Monte Carlo Methods Direct Simulation Prof Dr Michael Mascagni E mail mascagni math ethz ch http www cs fsu edu mascagni Seminar f r Angewandte Mathematik Eidgen ssische Technische Hochschule CH 8044 Z rich Switzerland and Department of Computer Science and School of Computational Science Florida State University Tallahassee Florida 32306 4120 USA Direct Simulation Probabilistic Problem or Model Directly simulate the physical random processes of the original problem Replace complicated blocks with randomized outcomes neutronics Direct the simulation s random outcomes with random numbers Examples Controlling floodwater and construction of new dams on the Niles The quantity of water in the river varies randomly from season to season Use records of weather rainfall and water levels extending over many years Examine what may happen to the water if certain dams are built and certain possible policies of water control are exercised Evaluate artificial lake impact on people agriculture transportation antiquities Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 2 of 16 Direct Simulation Cont Examples Cont Telephone networks Computer networks Growth of an insect population on the basis of certain assumed vital statistics of survival and reproduction Service rates queuing theory Operations research problems Actuarial simulations Insurance risk from tabulated date mortality Risk assessment of investment portfolios Computer games Roadway design simulation War gaming Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 3 of 16 Direct Simulation of the Lifetime of Comets A Long period Comet Described as a sequence of elliptic orbits Sun at one focus of the orbit ellipse Energy of a comet is inversely proportional to the length of the semi major axis of the ellipse Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 4 of 16 Direct Simulation of the Lifetime of Comets Cont Behavior of the Comet Most of the time Moves at a great distance from the sun A relatively short time instantaneous Passes through the immediate vicinity of the sun and the planets At this instant the gravitational field of the planet perturbs the cometary energy by a random component Successive energy perturbations in suitable units of energy may be taken as independent normally distributed random variables 1 2 3 n 1 2 3 n can be normalized to for example a standard normal distribution N 0 1 Computing the perturbations exactly is daunting Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 5 of 16 Direct Simulation of the Lifetime of Comets Cont Comets under perturbation A comet starting with an energy G z0 has subsequent energies The process continues until the first occasion on which z changes sign negative bound state positive free state z0 z1 z0 1 z2 z1 2 Once z changes sign the comet departs on a hyperbolic orbit and is lost from the solar system Kepler s Third Law the time taken to describe an orbit with energy z is z 3 2 the total lifetime of the comet is zT is the first negative quantity in the sequence of z0 z1 Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 6 of 16 Direct Simulation of the Lifetime of Comets Cont Problem is to determine distribution of G given z0 Very difficult problem in theoretical mechanics Easy to simulate using probabilistic model Seek CDF Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 7 of 16 Direct Simulation of the Lifetime of Comets Cont Monte Carlo trick use analytic deterministic information when it is available Probability of escape in one orbit available from N 0 1 tables Know also Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 8 of 16 Direct Simulation of the Lifetime of Comets Cont Now we need to estimate N samples subsample out of N have T 1 Let 1 p g be the proportion of values in the subsample with G g Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 9 of 16 Direct Simulation of the Lifetime of Comets Cont Using conditional probability we have Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 10 of 16 A Robotics Problem Take symmetric concentric objects O1 and O2 in random orientations what is the distribution of the smallest angle needed to rotate one into coincidence with the other There is an analytic solution but for simulation need to generate random orientations via random 3x3 orthogonal matrices Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 11 of 16 A Robotics Problem Cont Let Q0 x y z where the vectors x y z are uniformly distributed on S2 x x1 x2 x3 x 1 is uniform on S2 when x1 x2 x3 are i i d N 0 1 random variables Similarly define y y1 y2 y3 y 1 Take y y Px 1 P2 1 2 where P x y Gram Schmidt procedure Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 12 of 16 A Robotics Problem Cont Finally let z x x y which is orthogonal to the others Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 13 of 16 Dimensional Analysis Consider the Traveling Salesman problem given n towns to visit no order minimize the Hamiltonian path through the Euclidean weighted complete graph Let l be the length of the shortest path A total area of region containing cities n A is the density of cities Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 14 of 16 Dimensional Analysis Cont Assume l Aa n A b nbA a b Units l length n dimensionless number A length 2 Implies a b Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 15 of 16 Dimensional Analysis Cont Multiply the area by f while keeping the density constant l is also multiplied by f fA replaces A fn replaces n fl replaces l l nbA1 2 implies fl fbnbf1 2A1 2 so b l k nA 1 2 strictly via dimensional analysis Prof Dr Michael Mascagni Advanced Monte Carlo Methods Direct Simulation Slide 16 of 16


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