Bulletin of the American Physical Society
22nd Biennial Conference of the APS Topical Group on Shock Compression of Condensed Matter
Volume 67, Number 8
Monday–Friday, July 11–15, 2022; Anaheim, California
Session G00: Poster Session (5:30pm-7:30pm PDT)
5:30 PM,
Monday, July 11, 2022
Anaheim Marriott
Room: Platinum 7-10
Abstract: G00.00020 : Predicting Velocimetry Curves from Photonic Doppler Velocimetry (PDV) Signals using Neural Networks*
Presenter:
Keara G Frawley
(Georgia Institute of Technology)
Authors:
Keara G Frawley
(Georgia Institute of Technology)
Harikrishna Sahu
(Georgia Institute of Technology)
Naresh N Thadhani
(Georgia Institute of Technology)
Rampi Ramprasad
(Georgia Institute of Technology)
The goal of this work is to establish velocimetry curve consistency between multiple PDV probes through use of a Neural Network (NN) model, thereby reducing the uncertainties and errors associated with the current processing methods. The raw PDV signal is pre-processed and fed into the NN model with hidden layers, which outputs the predicted velocimetry curve. The work specifically utilizes LSTM (Long Short-Term Memory) Recurrent Neural Networks (RNNs). The method is expected to predict optimum outputs for different materials if it is fed a wide range of training data, because it learns from past inferences. The RNN model description and results obtained to date will be presented.
*This work was supported by the Department of Defense (DoD) through the National Defense Science and Engineering Graduate Fellowship (NDSEG) Fellowship.
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