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The approach is based on a high-order Taylor series expansion of the initial value problem of the differential schemes and the usage of automatic differentiation tools.
The adjoint code is completely generated using symbolic preprocessing and automatic differentiation tools which allow flexibility and require minimal user intervention.
In this study we report on initial efforts to apply integrated adjoint-based computational analysis and automatic differentiation tools to begin to address these issues.
Automatic differentiation tools are selectively used to develop the discrete adjoint, which make for a much shorter implementation time and greatly reduce the probability of programming errors.
These two genotypes were consistently amplified, suggesting the potential unique gene sequences on different plasmid copies and possibly providing differentiation tools.
With the antigenic analysis of all HA and NA subtypes, a comprehensive picture of AIV HA antigenicity can be established which can be used to design highly specific differentiation tools.
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The discrete adjoint solver is obtained with the aid of an automatic differentiation tool, TAPENADE.
The gradient information is supplied by the automatic differentiation tool Adifor by Argonne National Laboratory.
Analytical expressions for sensitivities are obtained by an automatic differentiation tool.
The required weights are the sensitivities of the goal with respect to local model errors; these sensitivities are calculated automatically with an Algorithmic Differentiation tool applied to the model's source code.
ADIC2 is the successor of the ADIC differentiation tool, which supports forward mode AD of C and a small subset of C++.
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