Simulation-Based Parallel TrainingDownload PDF

Published: 21 Oct 2022, Last Modified: 05 May 2023AI4Science PosterReaders: Everyone
Keywords: parallel training framework, online deep learning, numerical simulations, large scale
Abstract: Numerical simulations are ubiquitous in science and engineering. Machine learning for science investigates how artificial neural architectures can learn from these simulations to speed up scientific discovery and engineering processes. Most of these architectures are trained in a supervised manner. They require tremendous amounts of data from simulations that are slow to generate and memory greedy. In this article, we present our ongoing work to design a training framework that alleviates those bottlenecks. It generates data in parallel with the training process. Such simultaneity induces a bias in the data available during the training. We present a strategy to mitigate this bias with a memory buffer. We test our framework on the multi-parametric Lorenz's attractor. We show the benefit of our framework compared to offline training and the success of our data bias mitigation strategy to capture the complex chaotic dynamics of the system.
TL;DR: We propose a framework to train on-the-fly artificial neural architectures on simulation data generated in parallel.
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