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Found 4 papers, 2 papers with code
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Deep learning is a promising tool to determine the physical model that describes our universe.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.
Accelerating HPC codes on Intel(R) Omni-Path Architecture networks: From particle physics to Machine Learning
This displays how a factor of ten speedup in strongly scaled distributed machine learning could be achieved when synchronous stochastic gradient descent is massively parallelised with a fixed mini-batch size.
