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This may increase the overall numerical complexity of the model, but as the regression matrix will have a sparse structure, this complexity may be reduced using sparse matrix methods for computation. In Fig. 4.1, we see one example of a static polynomial fitted with local basis functions on ...
A surrogate model can be thought of as a “regression” to a set of data, where the data are a set of input–output pairings obtained by evaluating a black-box model of the complex system (Caballero and Grossmann, 2008; Eason and Cremaschi, 2014). Maintaining fidelity while being ...
We present a direct method for solving general systems of polynomial equations based on quantum annealing, and we validate this method using a system of second-order polynomial equations solved on a commercially available quantum annealer. We then demonstrate applications for linear regression, and ...
Least Angle Regression SP: Subspace Pursuit BCS: Bayesian Compressive Sensing DoE: Design of Experiment LOO: Leave-One-Out PDF: Probability Density Function CDF: Cumulative Distribution Function Ent-PCE: Entropy-based re-optimization sparse PCE HEnt-PCE: Hybrid entropy-based comprehensive ...
However, for short signals (like N≤500), this problem was solved by taking the time instants not as integers, but linearly spaced values from 1/N to 1, as the authors of [9] did. This article shows that the simple idea of shifting to orthonormal polynomials solves the two problems ...
If the observed data is unreliable, regression may be preferable because a regression model alleviates anomalies. But here we assume the observed data is reliable and valuable. This is particularly true when the data is obtained from numerical simulations. On the other hand, linear interpolation is...
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