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Full-Text Articles in Physics
M-Cubes: An Efficient And Portable Implementation Of Multi-Dimensional Integration For Gpus, Ioannis Sakiotis, Kamesh Arumugam, Marc Paterno, Desh Ranjan, Balŝa Terzić, Mohammad Zubair
M-Cubes: An Efficient And Portable Implementation Of Multi-Dimensional Integration For Gpus, Ioannis Sakiotis, Kamesh Arumugam, Marc Paterno, Desh Ranjan, Balŝa Terzić, Mohammad Zubair
Computer Science Faculty Publications
The task of multi-dimensional numerical integration is frequently encountered in physics and other scientific fields, e.g., in modeling the effects of systematic uncertainties in physical systems and in Bayesian parameter estimation. Multi-dimensional integration is often time-prohibitive on CPUs. Efficient implementation on many-core architectures is challenging as the workload across the integration space cannot be predicted a priori. We propose m-Cubes, a novel implementation of the well-known Vegas algorithm for execution on GPUs. Vegas transforms integration variables followed by calculation of a Monte Carlo integral estimate using adaptive partitioning of the resulting space. mCubes improves performance on GPUs by maintaining relatively …
Machine Learning-Based Event Generator For Electron-Proton Scattering, Y. Alanazi, P. Ambrozewicz, M. Battaglieri, A.N. Hiller Blin, M. P. Kuchera, Y. Li, T. Liu, R. E. Mcclellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, L. Velasco
Machine Learning-Based Event Generator For Electron-Proton Scattering, Y. Alanazi, P. Ambrozewicz, M. Battaglieri, A.N. Hiller Blin, M. P. Kuchera, Y. Li, T. Liu, R. E. Mcclellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, L. Velasco
Computer Science Faculty Publications
We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to construct a vertex-level event generator free of theoretical assumptions about femtometer scale physics. Our framework includes a GAN-based detector folding as a fast-surrogate model that mimics detector simulators. The framework is tested and validated on simulated inclusive deep-inelastic scattering data along with existing parametrizations for detector simulation, with uncertainty quantification based on a statistical bootstrapping technique. Our results provide for the first time a realistic proof of concept to mitigate theory bias in inferring vertex-level event …