Up to two billion times acceleration of scientific simulations with deep neural architecture search
CoRR abs/2001.08055 (2020)
Abstract:
Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully accelerates simulations by up to 2 billion times in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.Enhanced Fluorescence from X-Ray Line Coincidence Pumping
Chapter in X-Ray Lasers 2018, Springer Nature 241 (2020) 29-35
Laser produced electromagnetic pulses: generation, detection and mitigation
High Power Laser Science and Engineering Cambridge University Press (CUP) 8 (2020) e22
Axion-like-particle decay in strong electromagnetic backgrounds
Journal of High Energy Physics Springer 2019:12 (2019) 162
Coordination changes in liquid tin under shock compression determined using in situ femtosecond x-ray diffraction
Applied Physics Letters AIP Publishing 115:26 (2019) 264101