Probability And Partial Differential Equations In Modern Applied Mathematics
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Probability and Partial Differential Equations in Modern Applied Mathematics
Author: Edward C. Waymire
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-06-14
"Probability and Partial Differential Equations in Modern Applied Mathematics" is devoted to the role of probabilistic methods in modern applied mathematics from the perspectives of both a tool for analysis and as a tool in modeling. There is a recognition in the applied mathematics research community that stochastic methods are playing an increasingly prominent role in the formulation and analysis of diverse problems of contemporary interest in the sciences and engineering. A probabilistic representation of solutions to partial differential equations that arise as deterministic models allows one to exploit the power of stochastic calculus and probabilistic limit theory in the analysis of deterministic problems, as well as to offer new perspectives on the phenomena for modeling purposes. There is also a growing appreciation of the role for the inclusion of stochastic effects in the modeling of complex systems. This has led to interesting new mathematical problems at the interface of probability, dynamical systems, numerical analysis, and partial differential equations. This volume will be useful to researchers and graduate students interested in probabilistic methods, dynamical systems approaches and numerical analysis for mathematical modeling in the sciences and engineering.
Probability and Partial Differential Equations in Modern Applied Mathematics
"Probability and Partial Differential Equations in Modern Applied Mathematics" is devoted to the role of probabilistic methods in modern applied mathematics from the perspectives of both a tool for analysis and as a tool in modeling. There is a recognition in the applied mathematics research community that stochastic methods are playing an increasingly prominent role in the formulation and analysis of diverse problems of contemporary interest in the sciences and engineering. A probabilistic representation of solutions to partial differential equations that arise as deterministic models allows one to exploit the power of stochastic calculus and probabilistic limit theory in the analysis of deterministic problems, as well as to offer new perspectives on the phenomena for modeling purposes. There is also a growing appreciation of the role for the inclusion of stochastic effects in the modeling of complex systems. This has led to interesting new mathematical problems at the interface of probability, dynamical systems, numerical analysis, and partial differential equations. This volume will be useful to researchers and graduate students interested in probabilistic methods, dynamical systems approaches and numerical analysis for mathematical modeling in the sciences and engineering.
Modern Applied Mathematics
This comprehensive volume introduces educational units dealing with important topics of modern applied mathematics. Chapters include comprehensive information on different topics such as: Methods of Approximation for Mapping in Probability Spaces, Mathematical Modelling of Seismic Sources, Climate Variability, Geometry of Differential Equations, Modelling of Particle-Driven Gravity Currents, Impulsive Free-Surface Flows, Internal Wave Propagation, Isogroups and Exact Solutions of Higher Order Boltzman Equation, Molecular and Particle Modelling, Asymptotic Behaviour of Solutions of Nonlinear Partial Differential Equations, Mixed Boundary Value Problems, Dual Integral Equations, Dual Series Equations and their Applications, Evolutionary Mechanisms of Organization in Complex Systems, Zero-Sum Differential Games, Bernoulli Convolutions, Probability Distribution Functions, O.D.E. Approach to Stochastic Approximation, Bayesian Inference on the Long Range Dependence.