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SciAI4Industry – Solving PDEs for industry-scale problems with deep learning

Philipp A. Witte, Russell J. Hewett, Kumar Saurabh, AmirHossein Sojoodi, Ranveer Chandra
Microsoft, Redmond, USA
arXiv:2211.12709 [cs.DC], (23 Nov 2022)

@misc{https://doi.org/10.48550/arxiv.2211.12709,

   doi={10.48550/ARXIV.2211.12709},

   url={https://arxiv.org/abs/2211.12709},

   author={Witte, Philipp A. and Hewett, Russell J. and Saurabh, Kumar and Sojoodi, AmirHossein and Chandra, Ranveer},

   keywords={Distributed, Parallel, and Cluster Computing (cs.DC), Artificial Intelligence (cs.AI), Computational Physics (physics.comp-ph), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences},

   title={SciAI4Industry — Solving PDEs for industry-scale problems with deep learning},

   publisher={arXiv},

   year={2022},

   copyright={Creative Commons Attribution 4.0 International}

}

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Solving partial differential equations with deep learning makes it possible to reduce simulation times by multiple orders of magnitude and unlock scientific methods that typically rely on large numbers of sequential simulations, such as optimization and uncertainty quantification. Two of the largest challenges of adopting scientific AI for industrial problem settings is that training datasets must be simulated in advance and that neural networks for solving large-scale PDEs exceed the memory capabilities of current GPUs. We introduce a distributed programming API in the Julia language for simulating training data in parallel on the cloud and without requiring users to manage the underlying HPC infrastructure. In addition, we show that model-parallel deep learning based on domain decomposition allows us to scale neural networks for solving PDEs to commercial-scale problem settings and achieve above 90% parallel efficiency. Combining our cloud API for training data generation and model-parallel deep learning, we train large-scale neural networks for solving the 3D Navier-Stokes equation and simulating 3D CO2 flow in porous media. For the CO2 example, we simulate a training dataset based on a commercial carbon capture and storage (CCS) project and train a neural network for CO2 flow simulation on a 3D grid with over 2 million cells that is 5 orders of magnitudes faster than a conventional numerical simulator and 3,200 times cheaper.
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