Speeding up and reducing memory usage for scientific machine learning via mixed precision
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Publication:6566075
DOI10.1016/j.cma.2024.117093MaRDI QIDQ6566075
Joel Hayford, Jacob Goldman-Wetzler, Lu Lu, Eric H. Wang
Publication date: 3 July 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
computational efficiencypartial differential equationsmixed precisionphysics-informed neural networksscientific machine learningdeep operator networks
Artificial neural networks and deep learning (68T07) Roundoff error (65G50) Parallel numerical computation (65Y05)
Cites Work
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- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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- DeepXDE: A Deep Learning Library for Solving Differential Equations
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- fPINNs: Fractional Physics-Informed Neural Networks
- Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
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