Accelerating Science with Generative Adversarial Networks
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Presentation at NERSC Data Day 2017 at Lawrence Berkeley National Laboratory on the potential of Generative Adversarial Networks to speed up scientific simulation and empower scientists and researchers.
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Accelerating Science with Generative Adversarial Networks
- 1. Michela Paganini (Yale) Accelerating Science with Generative Adversarial Networks 1 NERSC Data Day September 19, 2017
- 2. The Rise of AI 2 • Deep Learning research has achieved impressive results both in academic and industry contexts Finding extreme weather events in climate simulations Classifying new physics events at the Large Hadron Collider Computer Vision Sentiment analysis Language understanding • Deep Learning applications have the potential to revolutionize science
- 3. Generative Modeling 3 Build a generative model with probability distribution
- 4. Generative Modeling for Science 4 Why study generative models? “After all, when applied to images, such models seem to merely provide more images, and the world has no shortage of images.”
- 5. Generative Modeling for Science 4 Why study generative models? “After all, when applied to images, such models seem to merely provide more images, and the world has no shortage of images.” except that we do! ATLAS grid consumption LSST-‐DESC cosmological simulation run on 16384 of Titan's GPU-‐enhanced nodes
- 6. Taxonomy of Generative Models 5 From I. Goodfellow Maximum Likelihood …
- 7. Taxonomy of Generative Models 5 From I. Goodfellow Maximum Likelihood Explicit density …
- 8. Taxonomy of Generative Models 5 From I. Goodfellow Maximum Likelihood Explicit density … Tractable density Fully visible belief nets: NADE MADE PixelRNN Change of variables models (nonlinear ICA)
- 9. Taxonomy of Generative Models 5 From I. Goodfellow Maximum Likelihood Explicit density … Tractable density Fully visible belief nets: NADE MADE PixelRNN Change of variables models (nonlinear ICA) Approximate density Variational VAE Markov Chain Boltzmann machine
- 10. Taxonomy of Generative Models 5 From I. Goodfellow Maximum Likelihood Explicit density Implicit density … Tractable density Fully visible belief nets: NADE MADE PixelRNN Change of variables models (nonlinear ICA) Approximate density Variational VAE Markov Chain Boltzmann machine
- 11. Taxonomy of Generative Models 5 From I. Goodfellow Maximum Likelihood Explicit density Implicit density … Tractable density Fully visible belief nets: NADE MADE PixelRNN Change of variables models (nonlinear ICA) Approximate density Variational VAE Markov Chain Boltzmann machine Markov Chain Direct GSN GAN
- 12. Taxonomy of Generative Models 5 From I. Goodfellow Maximum Likelihood Explicit density Implicit density … Tractable density Fully visible belief nets: NADE MADE PixelRNN Change of variables models (nonlinear ICA) Approximate density Variational VAE Markov Chain Boltzmann machine Markov Chain Direct GSN GAN
- 14. Generative Adversarial Networks 6 From M. Mustafa
- 15. CaloGAN 7 • Fast & accurate simulation of calorimeter showers in a three-‐layer heterogeneously segmented LAr+lead detector • Ad-‐hoc design to fit Physics data: • sparsity • high dynamic range • highly location-‐dependent features • Soon to be integrated in Geant (Paganini et al., arXiv:1705.02355) 3x96 12x12 12x6
- 16. CaloGAN 8 Energy (Paganini et al., arXiv:1705.02355) Average energy deposition per calorimeter layer in the GEANT4 training dataset (top) and in the GAN generated dataset (bottom) Ten positron showers generated by varying shower energy in equal intervals while holding all other latent codes fixed. The three rows are the shower representations in the three calorimeter layers. The energies of showers in the green box were within the range of the training dataset, while the ones in the red box are in the extrapolation regime. • Realistic average and individual images • Diverse samples • Conditional generation based on physical attributes • Parameter interpolation and extrapolation
- 17. CosmoGAN 9 • Generate convergence maps of a particular ΛCDM model • Match summary statistics of training dataset: • 1st order statistic (pixel intensity) • 2nd order correlations • measurement of the non-‐ Gaussian corrections (Mustafa et al., arXiv:1706.02390)
- 18. Conclusions & Outlook 10 • GANs: promising technology to accelerate scientific simulation • Potential to save millions of CPU hours and dollars • In scientific applications: • Unique considerations and constraints • Unique evaluation methods for generative models • Outstanding challenges require continued R&D to meet fidelity demands
- 19. Thanks! 11