Parameter Estimation for PDEs using Stochastic Methods - Roxana Elena Tanase
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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 ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Roxana Elena Tanase |
|---|---|
| Izdevējs | LAP LAMBERT Academic Publishing |
| Izlaides gads | 2019 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783659927324 |