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These lectures concentrate on (nonlinear) stochastic partial differential equations (SPDE) of evolutionary type. All kinds of dynamics with stochastic influence in nature or man-made complex systems can be modelled by such equations. To keep the technicalities minimal we confine ourselves to the case where the noise term is given by a stochastic integral w.r.t. a cylindrical Wiener process.But all results can be easily generalized to SPDE with more general noises such as, for instance, stochastic integral w.r.t. a continuous local martingale. There are basically three approaches to analyze SPDE: the ‘martingale measure approach’, the ‘mild solution approach’ and the ‘variational approach’. The purpose of these notes is to give a concise and as self-contained as possible an introduction to the ‘variational approach’. A large part of necessary background material, such as definitions and results from the theory of Hilbert spaces, are included in appendices.
INDICE: Motivation, Aims and Examples.- Stochastic Integral in Hilbert spaces.- Stochastic Differential Equations in Finite Dimensions.- A Class of Stochastic Differential Equations in Banach Spaces.- Appendices: The Bochner Integral.- Nuclear and Hilbert-Schmidt Operators.- Pseudo Invers of Linear Operators.- Some Tools from Real Martingale Theory.- Weak and Strong Solutions: the Yamada-Watanabe Theorem.- Strong, Mild and Weak Solutions.
Researchers and graduate students in mathematics, physics and economics
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