11 citations · 12 across the 13 of their papers we have counts for
14 papers
A Machine Learning Model for Predicting Progressive Crack Extension based on DCPD Fatigue Data
Jacob Keesler-Evans, Ansan Pokharel, Robert Tempke +1
Time history data collected from a Direct Current Potential Drop (DCPD) fatigue experiment at a range of temperatures was used to train a Bidirectional Long-Short Term Memory Neura…
Machine Learning Approach for Transforming Scattering Parameters to Complex Permittivity
Robert Tempke, Liam Thomas, Christina Wildfire +2
This study investigates the application of an artificial neural network to predict the complex dielectric properties of granular catalysts commonly used in microwave reaction chemi…
Experimental Investigation of Crack Jumps during Initiation and Growth in IN718
Joel Lindsay, Stefanos Papanikolaou, Terence Musho
The following study investigates the statistical nature of crack jumps during fatigue of Inconel 718. In-situ measurement in atmospheric air of the crack length at several loading…
GHz Dielectric Measurement of Powdery TiO
Robert Tempke, Christina Wildfire, Dushyant Shekhawat +1
This study experimentally determines the dielectric properties of a powdery titanium (II) oxide (TiO) material within the microwave range (0.1-13.5 GHz). The properties were determ…
Dielectric Measurement of Powdery Materials using a Coaxial Transmission Line
Robert Tempke, Christina Wildfire, Dushyant Shekhawat +1
The following study investigates the use of a coaxial transmission line for determining the properties of powdery dielectric materials (1-10GHz). Four powdery materials with dielec…
A Machine Learning Approach for Increased Throughput of Density Functional Theory Substitutional Alloy Studies
Alhassan S. Yasin, Terence D. Musho
In this study, a machine learning-based technique is developed to reduce the computational cost required to explore large design spaces of substitutional alloys. The first advancem…