AirNet-SNL: Generalized End-to-End Training of Iterative Reconstruction and Deep Neural Network Regularization for Sparse-Data CT.
Abstract not provided.
Lee, Dennis J↗
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Results for “deep networks”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Detection and Identification of Human Locomotion by Distributed Acoustic Sensing with Deep Neural Networks
This paper reports intrusion detection using distributed acoustic sensors based on phase- sensitive enhanced Rayleigh scattering optical time-domain reflectometry. Vibration data induced by human locomotion was analyzed by convolution neural networks and achieve over 76.25% accuracy on human identification.
Peng, Zhaoqiang↗
Using Deep Neural Networks to Extract Fireball Parameters from Infrared Spectral Data
Use spectrometers to monitor high explosive (HE) events in infrared region: Spectrometers measure radiance in many (100s) of spectral bands; Sensors give information on the fireball such as temperature, size, soot quantity and gas species concentrations (CO, CO₂, H₂O, etc.).
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Phenomenological Demonstration of Deep Neural Networks in the Search for BSM Physics w/ LArTPCs [Poster]
Explore the source record for details and available documents.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
AirNet-SNL: End-To-End Training of Iterative Reconstruction and Deep Neural Network Regularization for Sparse-Data XPCI CT.
Abstract not provided.
Lee, Dennis↗
Solar Transposition Modeling via Deep Neural Networks with Sky Images.
Abstract not provided.
Pierce, Benjamin↗
Impact of Safeguards Measurement Errors on Deep Neural Networks.
Abstract not provided.
Shoman, Nathan↗
Longitudinal Suicide Risk Prediction and Interpretation with Deep Neural Networks and Shapely Additive Explanations.
Abstract not provided.
Levin, Drew↗
Robust architectures, initialization, and training for deep neural networks via the adaptive basis interpretation.
Abstract not provided.
Gulian, Mamikon↗
Using Deep Neural Networks to Predict Material Types in Conditional Point Sampling Applied to Markovian Mixture Models.
Abstract not provided.
Olson, Aaron↗
Deep Neural Networks as Surrogates for Intractable Constraints and Problem Dimension Reduction: SC ACOPF.
Abstract not provided.
Kilwein, Zachary↗
Energy Efficient Deep Neural Network Processing: Digital CMOS Limits and Prospects for Analog In-Memory Computing.
Abstract not provided.
Marinella, Matthew↗
Facilitating Atmospheric Source Inversion via Deep Operator Network Surrogates.
Abstract not provided.
Gulian, Mamikon↗
Adapting Multigrid-in-Time to Train Deep Neural Networks [Slides]
Abstract not provided.
97 MATHEMATICS AND COMPUTING↗
Deep Neural Network Informed Markov Chain Monte Carlo Methods
In subsurface flow modeling, quantifying the uncertainty of model parameters and the corresponding uncertainly on output quantities is a crucial task for groundwater management. Markov chain Monte Carlo (MCMC) methods can take advantage of observed data to estimate parameters in a Bayesian setting. However, MCMC can be slow to converge and produce highly correlated samples when the dimensions of the parameters is high. Using gradients for the posterior distribution can help samplers explore the parameter space more efficiently, but obtaining gradients can be computationally challenging.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗