Probabilistic Methods in Applied Physics (Lecture Notes in Physics)

[Springer] ✓ Probabilistic Methods in Applied Physics (Lecture Notes in Physics) ✓ Download Online eBook or Kindle ePUB. Probabilistic Methods in Applied Physics (Lecture Notes in Physics) this is a useful reference volume from researchers who amassed experience with simulation according to tapehead/2. This is a collection of papers on numerical methods for simulation of various stochastic systems. The most relevant for me personally was the paper by Denis Talay which details the use of various approximation schemes for solving SDEs numerically. It makes emphasis of Milstein schemes, and compares them to Euler schemes which are substantially less accurate in the specific example

Probabilistic Methods in Applied Physics (Lecture Notes in Physics)

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Rating : 4.25 (753 Votes)
Asin : 3662140063
Format Type : paperback
Number of Pages : 393 Pages
Publish Date : 2014-11-13
Language : English

DESCRIPTION:

Language Notes Text: English, French

In particular the volume covers: simulation, stability theory, Lyapounov exponents, stochastic modelling, statistics on trajectories, parametric stochastic control, Fokker Planck equations, and Wiener filtering.. The articles present methods allowing concrete calculations without neglecting the mathematical foundations. They address physicists and engineers interested in scientific computation and simulation techniques. This book is an outcome of a European collaboration on applications of stochastical methods to problems of science and engineering

"this is a useful reference volume from researchers who amassed experience with simulation" according to tapehead/2. This is a collection of papers on numerical methods for simulation of various stochastic systems. The most relevant for me personally was the paper by Denis Talay which details the use of various approximation schemes for solving SDEs numerically. It makes emphasis of Milstein schemes, and compares them to Euler schemes which are substantially less accurate in the specific examples discussed in that chapter. There are also several chapters that show how to generate paths of vector valued stochasti

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