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Parameter Estimation for PDEs using Stochastic Methods - Roxana Elena Tanase

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2019-02-14
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The aim of this book is to compare the efficiency of different algorithms on estimating parameters that arise in partial differential equations: Kalman Filters (Ensemble Kalman Filter, Stochastic Collocation Kalman Filter, Karhunen-Lo`eve Ensemble Kalman Filter, Karhunen- Lo`eve Stochastic Collocation Kalman Filter), Markov-Chain Monte Carlo sampling schemes and Adjoint variable-based method. We also presen ... Visas aprašymas

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The aim of this book is to compare the efficiency of different algorithms on estimating parameters that arise in partial differential equations: Kalman Filters (Ensemble Kalman Filter, Stochastic Collocation Kalman Filter, Karhunen-Lo`eve Ensemble Kalman Filter, Karhunen- Lo`eve Stochastic Collocation Kalman Filter), Markov-Chain Monte Carlo sampling schemes and Adjoint variable-based method. We also present the theoretical results for stochastic optimal control for problems constrained by partial differential equations with random input data in a mixed finite element form. We verify experimentally with numerical simulations using Adjoint variable-based method with various identification objectives that either minimize the expectation of a tracking cost functional or minimize the difference of desired statistical quantities in the appropriate Lp norm.

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Autorius Roxana Elena Tanase
Leidėjas LAP LAMBERT Academic Publishing
Išleidimo metai 2019
Viršelio tipas Minkšti viršeliai
EAN 9783659927324
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66,82 € 89,10 €