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GTC ON-DEMAND

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Abstract:
Computer simulations offer great insight into complex, dynamical systems but can be difficult to navigate through a large set of control/design parameters. Deep learning methods, applied on fast GPUs, can provide an ideal way to improve scientific and engineering workflows. In this talk, Vic will discuss an application of machine learning to develop a fast-running surrogate model that captures the dynamics of an industrial multiphase fluid flow. He will also discuss an improved population search method that can help the analyst explore a high-dimensional parameter space to optimize production while reducing the model uncertainty.
Computer simulations offer great insight into complex, dynamical systems but can be difficult to navigate through a large set of control/design parameters. Deep learning methods, applied on fast GPUs, can provide an ideal way to improve scientific and engineering workflows. In this talk, Vic will discuss an application of machine learning to develop a fast-running surrogate model that captures the dynamics of an industrial multiphase fluid flow. He will also discuss an improved population search method that can help the analyst explore a high-dimensional parameter space to optimize production while reducing the model uncertainty.  Back
 
Topics:
HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8828
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