Osborne Research Group
Computational Methods for Energy Applications
Dr. Andy Osborne’s research has a focus on applying machine learning and high performance computing in the context of low carbon energy systems, with an emphasis on nuclear energy.
Research efforts cover topics including advanced nuclear reactors and fuel cycles, technologies for spent fuel monitoring, and machine learning driven methods for reactor simulations, nuclear data and analytical radiochemistry. Group activities also include geospatial analytics in nuclear security and renewable energy applications.
Contact
Brown Building W370A
The Colorado School of Mines
Golden, CO 80401
osbornea@mines.edu
Machine Learning
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Uncertainty quantification in nuclear science & engineering using ensemble neural networks
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Accelerating pebble bed reactor simulations using K-means clustering
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Energy group optimization with artificial neural networks and random forest regressors
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Nuclear data compression with autoencoder neural networks
Reactor Modeling & Simulation
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Ray tracing for accelerating neutron transport simulations
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Nonlinear dynamics of traveling wave reactors
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Monte Carlo and deterministic reactor simulations
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Reactor thermal-hydrauics
Geospatial Analytics
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Countering nuclear terrorism
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Browser & mobile applications for geospatial energy analytics
