Fuzzy engineering
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Automatic differentiation of algorithms: from simulation to optimization
Automatic differentiation of algorithms: from simulation to optimization
Adifor 2.0: Automatic Differentiation of Fortran 77 Programs
IEEE Computational Science & Engineering
Coalescing executions for fast uncertainty analysis
Proceedings of the 33rd International Conference on Software Engineering
GMG - A guaranteed global optimization algorithm: Application to remote sensing
Mathematical and Computer Modelling: An International Journal
White box sampling in uncertain data processing enabled by program analysis
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
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The objective is to determine confidence limits for the outputs of a mathematical model of a physical system that consists of many interacting computer codes. Each code has many modules that receive inputs, write outputs, and depend on parameters. Several of the outputs of the system of codes can be compared to sensor measurements. The outputs of the system are uncertain because the inputs and parameters of the system are uncertain. The method uses sensitivities to propagate uncertainties from inputs to outputs through the complex chain of modules. Furthermore, the method consistently combines sensor measurements with model outputs to simultaneously obtain best estimates for model parameters and reduce uncertainties in model outputs. The method was applied to a test case where ADIFOR2 was used to calculate sensitivities for the radiation transport code MODTRAN.