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Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineering.
Learning Neural PDE Solvers with Convergence Guarantees
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Back in the Saddle Again: A Computer Assisted Study of the Kuramoto–Sivashinsky Equation
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Predicting Chaos for Infinite Dimensional Dynamical Systems: The Kuramoto-Sivashinsky Equation, A Case Study
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Solving ordinary differential equations. II, volume 14 of Springer Series in Computational Mathematics
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Lectures in Computational Fluid Dynamics of Incompressible Flow: Mathematics, Algorithms and Implementations
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Generative Adversarial Nets
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Gaussian error linear units (gelus)
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JAX: composable transformations of Python+NumPy programs. 2018. Software URL: http://github.com/google/jax
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Group Normalization
Yuxin Wu and Kaiming He. 2018 · 2018
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Learning data-driven discretizations for partial differential equations
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Prediction of aerodynamic flow fields using convolutional neural networks
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Progressive deblurring of diffusion models for coarse-to-fine image synthesis
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Learning Chaotic Dynamics in Dissipative Systems
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Progressive Distillation for Fast Sampling of Diffusion Models
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PDEBench: An Extensive Benchmark for Scientific Machine Learning
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TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation
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