Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/47523
Title: Scaling and sensitivity analysis of machine learning regression on periodic functions
Authors: Trahan, Corey J.
Rivera, Peter G.
Keywords: Computer algorithms
Machine learning
Ocean circulation
Periodic functions
Regression analysis
Tidal currents
Tides
Publisher: Engineer Research and Development Center (U.S.)
Series/Report no.: Technical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC/ITL TR-23-4
Abstract: In this report we document the scalability and sensitivity of machine learning (ML) regression on a periodic, highly oscillating, and 𝐶∞ function. This work is motivated by the need to use ML regression on periodic problems such as tidal propagation. In this work, TensorFlow is used to investigate the machine scalability of a periodic function from one to three dimensions. Wall clock times for each dimension were calculated for a range of layers, neurons, and learning rates to further investigate the sensitivity of the ML regression to these parameters. Lastly, the stochastic gradient descent and Adam optimizers wall clock timings and sensitivities were compared.
Description: Technical Report
Gov't Doc #: ERDC/ITL TR-23-4
Rights: Approved for Public Release; Distribution is Unlimited
URI: https://hdl.handle.net/11681/47523
http://dx.doi.org/10.21079/11681/47523
Appears in Collections:Technical Report

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