Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
The Nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology, Volume I: Overcoming the Curse of Dimensionality: Linear Systems

The Nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology, Volume I: Overcoming the Curse of Dimensionality: Linear Systems

Cacuci, Dan Gabriel

Hardcover

General MathematicsPhysicsProbability & Statistics

ISBN10: 3030963632
ISBN13: 9783030963637
Publisher: Springer Nature
Published: Jul 20 2022
Pages: 362
Weight: 1.55
Height: 0.88 Width: 6.14 Depth: 9.21
Language: English

The computational models of physical systems comprise parameters, independent and dependent variables. Since the physical processes themselves are seldom known precisely and since most of the model parameters stem from experimental procedures which are also subject to imprecisions, the results predicted by these models are also imprecise, being affected by the uncertainties underlying the respective model. The functional derivatives (also called sensitivities) of results (also called responses) produced by mathematical/computational models are needed for many purposes, including: (i) understanding the model by ranking the importance of the various model parameters; (ii) performing reduced-order modeling by eliminating unimportant parameters and/or processes; (iii) quantifying the uncertainties induced in a model response due to model parameter uncertainties; (iv) performing model validation, by comparing computations to experiments to address the question does the modelrepresent reality? (v) prioritizing improvements in the model; (vi) performing data assimilation and model calibration as part of forward predictive modeling to obtain best-estimate predicted results with reduced predicted uncertainties; (vii) performing inverse predictive modeling; (viii) designing and optimizing the system.

1 different editions

Also available

Also from

Cacuci, Dan Gabriel

View All

Also in

Physics

View All