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Synthesis Modeling: The Intersection of HPC and Machine Learning
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Historically, numerical analysis has formed the backbone of supercomputing for decades by applying mathematical models of first-principle physics to simulate the behavior of systems from subatomic to galactic scale. Recently, scientists have begun experimenting with a relatively new approach to understand complex systems using machine learning (ML) predictive models, primarily Deep Neural Networks (DNN), trained by the virtually unlimited data sets produced from traditional analysis and direct observation.
See how early results indicate that these “synthesis models”, combining ML and traditional simulation, can:
- Improve accuracy
- Accelerate time to solution
- Significantly reduce costs