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An Automatic (or Algorithmic) Differentiation (AD) tool takes a user's model coded in an imperative programming language, and from this automatically generates new code that will evaluate analytical partial derivative values for this model.
A possible method to address this issue is to replace the analytical partial derivatives by a numerical method; however, this may require substantial computational resources.
Parameters were optimized using the Levenberg Marquardt iterative method (Marquardt 1963) with automatic computation of the analytical partial derivatives.
Parameters were optimised using the Levenberg-Marquardt iterative method with automatic computation of the analytical partial derivatives [ 32, 33].
Parameters were optimized using the Levenberg Marquardt iterative method with automatic computation of the analytical partial derivatives.
The Jacobian matrix is estimated from analytical partial parameter derivatives of all adjusted parameters calculated at each spectral point.
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Furthermore, a design approach based on analytical solution of partial differential equations is proposed.
Del Castillo's method is used to efficiently establish the analytical expression of partial gear ratios.
This relies on the model being linearly separable, enabling partial analytical marginalization, reducing the dimension of the parameter space.
A specific technique for numerical integration is verified on the basis of comparison with a partial analytical solution.
In this paper, the analytical first order partial derivatives of the objective function are deduced for 3D problems.
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