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In this study, pressure drop ( ) across air-cooled heat sinks (HSs) are predicted using an artificial neural network (ANN). A multilayer feed-forward ANN architecture with two hidden layers is developed. Backpropagation algorithm is used for training the network, and the accuracy of the network is evaluated by the root mean square error. The input data for training the neural network is prepared through three-dimensional simulation of air inside the channels of heat sinks using a computational fluid dynamics (CFD) approach. The developed ANN-based model in this study predicts with a high accuracy and within of the CFD-based data. The present study suggests that developing an ANN-based model with a high level of accuracy overcomes the limitations of physics-based correlations that their accuracy strongly depends on identifying and implementing key variables that affect the physics of a thermo-fluid phenomenon.
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Improved neural network Monte Carlo simulation
The algorithm for Monte Carlo simulation of parton-level events basedon an Artificial Neural Network (ANN) proposed in Ref.~ is used toperform a simulation of H\to 4\ell H → 4 ℓ decay. Improvements in the training algorithm have been implemented toavoid numerical instabilities. The integrated decay width evaluated bythe ANN is within 0.7% of the true value and unweighting efficiency of26% is reached. While the ANN is not automatically bijective betweeninput and output spaces, which can lead to issues with simulationquality, we argue that the training procedure naturally prefersbijective maps, and demonstrate that the trained ANN is bijective to avery good approximation.
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- Award ID(s):
- 2014071
- PAR ID:
- 10284157
- Date Published:
- Journal Name:
- SciPost Physics
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2542-4653
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.21468/SciPostPhys.10.1.023
