10,276 research outputs found
Automatic differentiation for gradient-based optimization of radiatively heated microelectronics manufacturing equipment
Automatic differentiation is applied to the optimal design of microelectronic manufacturing equipment. The performance of nonlinear, least-squares optimization methods is compared between numerical and analytical gradient approaches. The optimization calculations are performed by running large finite-element codes in an object-oriented optimization environment. The Adifor automatic differentiation tool is used to generate analytic derivatives for the finite-element codes. The performance results support previous observations that automatic differentiation becomes beneficial as the number of optimization parameters increases. The increase in speed, relative to numerical differences, has a limited value and results are reported for two different analysis codes
Deep Learning: A Tutorial
Our goal is to provide a review of deep learning methods which provide
insight into structured high-dimensional data. Rather than using shallow
additive architectures common to most statistical models, deep learning uses
layers of semi-affine input transformations to provide a predictive rule.
Applying these layers of transformations leads to a set of attributes (or,
features) to which probabilistic statistical methods can be applied. Thus, the
best of both worlds can be achieved: scalable prediction rules fortified with
uncertainty quantification, where sparse regularization finds the features.Comment: arXiv admin note: text overlap with arXiv:1808.0861
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