Speeding up and reducing memory usage for scientific machine learning
via mixed precision

Speeding up and reducing memory usage for scientific machine learning via mixed precision

30 January 2024

Jacob Goldman-Wetzler

ArXiv (abs)PDF HTML

Papers citing "Speeding up and reducing memory usage for scientific machine learning via mixed precision"

13 / 13 papers shown

Title
Machine Learning for Partial Differential Equations Steven L. Brunton J. Nathan Kutz AI4CE 78 21 0 30 Mar 2023
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks Chen-Chun Wu Min Zhu Qinyan Tan Yadhu Kartha Lu Lu 86 377 0 21 Jul 2022
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks Mitchell Daneker Zhen Zhang George Karniadakis Lu Lu CML 42 36 0 03 Feb 2022
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 97 520 0 09 Feb 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 500 2,448 0 18 Oct 2020
Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks K. Shukla P. C. D. Leoni J. Blackshire D. Sparkman George Karniadakis PINN AI4CE 78 232 0 07 May 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 248 2,153 0 08 Oct 2019
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 99 1,540 0 10 Jul 2019
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data Yinhao Zhu N. Zabaras P. Koutsourelakis P. Perdikaris PINN AI4CE 114 869 0 18 Jan 2019
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems Dongkun Zhang Lu Lu Ling Guo George Karniadakis UQCV 111 409 0 21 Sep 2018
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification Yinhao Zhu N. Zabaras UQCV BDL 111 646 0 21 Jan 2018
Visualizing the Loss Landscape of Neural Nets Hao Li Zheng Xu Gavin Taylor Christoph Studer Tom Goldstein 258 1,898 0 28 Dec 2017
Learning both Weights and Connections for Efficient Neural Networks Song Han Jeff Pool J. Tran W. Dally CVBM 313 6,700 0 08 Jun 2015