malign
发表于 2025-3-30 11:03:46
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弯曲的人
发表于 2025-3-30 15:26:19
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暴行
发表于 2025-3-30 17:47:09
Computing Covariance Matrices for Constrained Nonlinear Large Scale Parameter Estimation Problems UsIn the paper we show how, based on the preconditioned Krylov subspace methods, to compute the covariance matrix of parameter estimates, which is crucial for efficient methods of optimum experimental design.
abreast
发表于 2025-3-30 22:21:12
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色情
发表于 2025-3-31 02:08:02
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otic-capsule
发表于 2025-3-31 07:02:38
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自作多情
发表于 2025-3-31 12:18:54
Generalized Multilevel SQP-methods for PDAE-constrained Optimization Based on Space-Time Adaptive PDdifferential algebraic equations and point-wise control constraints. A newly developed generalized SQP-method is combined with an error based multilevel strategy and the state-of-the-art software package Kardos to allow the efficient resolution of different space and time scales in an adaptive manne
Atheroma
发表于 2025-3-31 13:21:06
Projection Based Model Reduction for Optimal Design of the Time-dependent Stokes System in particular if the underlying system of PDEs represents a multi-scale, multi-physics problem. Therefore, reduced order modeling techniques such as balanced truncation model reduction, proper orthogonal decomposition, or reduced basis methods are used to significantly decrease the computational co
CLEFT
发表于 2025-3-31 18:30:39
Automated Extension of Fixed Point PDE Solvers for Optimal Design with Bounded Retardationhnique that improves primal, dual feasibility and optimality simultaneously in each iteration step, thus coupling state and adjoint iteration and control/design update. Our goal is to obtain bounded retardation of this coupled iteration compared to the original one for the state, since the latter in
indignant
发表于 2025-4-1 01:39:54
A Globalized Semi-smooth Newton Method for Variational Discretization of Control Constrained Elliptiion of pde constrained optimization with control constraints special emphasis has to be placed on the implementation, convergence and globalization of the numerical algorithm. In the present work we address all these issues following . In particular we prove fast local convergence