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Found 4 papers, 1 papers with code
Hyper-Reduced Autoencoders for Efficient and Accurate Nonlinear Model Reductions
In this work, we propose and analyze a novel method that overcomes this disadvantage by training a neural network only on subsampled versions of the high-fidelity solution snapshots.
Parameterized Pseudo-Differential Operators for Graph Convolutional Neural Networks
The new layer achieves a test error rate of 0. 80% on the MNIST superpixel dataset, beating the closest reported rate of 0. 95% by a factor of more than 15%.
A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems
This work introduces a reformulation, called rank-2 Galerkin, of the Galerkin ROM for LTI dynamical systems which converts the nature of the ROM problem from memory bandwidth to compute bound.
Computational Physics Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing Mathematical Software Dynamical Systems
A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data using Unstructured Spatial Discretizations
Our custom graph convolution operators based on the available differential operators for a given spatial discretization effectively extend the application space of deep convolutional autoencoders to systems with arbitrarily complex geometry that are typically discretized using unstructured meshes.
