Parameter Estimation and Optimum Experimental Design for Differential Equation Models

DOI10.1007/978-3-642-30367-8_1zbMath1269.65014OpenAlexW121290872MaRDI QIDQ4928942

Johannes P. Schlöder, Hans Georg Bock, Stefan Körkel

Publication date: 19 June 2013

Published in: Contributions in Mathematical and Computational Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-30367-8_1

zbMATH Keywords

algorithmparameter estimationnonlinear differential-algebraic equationsvariance-covariance matrixGauss-Newton methodoptimum experimental designmultiple shootingboundary value problem method

Mathematics Subject Classification ID

Related Items (8)

A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems ⋮ A modified multiple shooting algorithm for parameter estimation in ODEs using adjoint sensitivity analysis ⋮ Optimum Experimental Design by Shape Optimization of Specimens in Linear Elasticity ⋮ Parameter estimation and experimental design for Hill-type muscles: impulses from optimization-based modeling ⋮ Hyper-differential sensitivity analysis for inverse problems constrained by partial differential equations ⋮ Krylov Approximation of Linear ODEs with Polynomial Parameterization ⋮ Optimal experimental design for prediction based on push-forward probability measures ⋮ Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: a review

Uses Software

• DAESOL-II

Cites Work

• Evaluating Derivatives
• Parameter Identification in One-Dimensional Partial Differential Algebraic Equations
• Optimal Measurement Methods for Distributed Parameter System Identification
• Numerical methods for optimal control problems in design of robust optimal experiments for nonlinear dynamic processes
• Gauss–Newton Methods for Robust Parameter Estimation
• An Optimal Scanning Sensor Activation Policy for Parameter Estimation of Distributed Systems
• Robustness Aspects in Parameter Estimation, Optimal Design of Experiments and Optimal Control
• On the Numerical Solution of Constrained Least-Squares Problems
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