Stochastic Partial Differential Equations and Applications: Proceedings of a Conference Held in Trento, Italy, September 30 - October 5, 1985
Giuseppe Da Prato, Luciano Tubaro
Springer, Mar 25, 1987 - Mathematics - 257 pages
Based on the proceedings of the International Conference on Stochastic Partial Differential Equations and Applications-V held in Trento, Italy, this illuminating reference presents applications in filtering theory, stochastic quantization, quantum probability, and mathematical finance and identifies paths for future research in the field. Stochastic Partial Differential Equations and Applications analyzes recent developments in the study of quantum random fields, control theory, white noise, and fluid dynamics. It presents precise conditions for nontrivial and well-defined scattering, new Gaussian noise terms, models depicting the asymptotic behavior of evolution equations, and solutions to filtering dilemmas in signal processing. With contributions from more than 40 leading experts in the field, Stochastic Partial Differential Equations and Applications is an excellent resource for pure and applied mathematicians; numerical analysts; mathematical physicists; geometers; economists; probabilists; computer scientists; control, electrical, and electronics engineers; and upper-level undergraduate and graduate students in these disciplines.
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Other editions - View all
Stochastic Partial Differential Equations and Applications
Giuseppe Da Prato,Luciano Tubaro
Limited preview - 2002
Applications approximation arbitrary argument assume assumptions Banach Banach space boundary bounded Brownian motion calculus coefficients compact complete compute consider constant convergence corresponding defined definition denote density derivatives diffusion processes distribution estimate example existence and uniqueness exists extend fact filtering Finally finite formula functions given gives Hilbert space holds hypotheses implies independent inequality infinite dimensional introduce Italy Lemma limit linear mapping Markov process martingale Math mean measure method Moreover multiplicative nonlinear norm Note observation obtain operator parabolic partial differential equations positive probability problem product integration Proof Proposition prove REFERENCES regularity remark respect satisfies semigroup sequence solution solve Springer-Verlag standard stochastic differential equations stochastic integral sufficiently Suppose Theorem Theorem 2.4 theory tion transformation Univ valued variation Wiener write zero