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Found 6 papers, 2 papers with code
Deep polytopic autoencoders for low-dimensional linear parameter-varying approximations and nonlinear feedback design
Polytopic autoencoders provide low-dimensional parametrizations of states in a polytope.
Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows
With the advancement of neural networks, there has been a notable increase, both in terms of quantity and variety, in research publications concerning the application of autoencoders to reduced-order models.
Going Deeper with Five-point Stencil Convolutions for Reaction-Diffusion Equations
In this work, we propose deep FCNNs that have large receptive fields to predict time evolutions with a time step larger than the threshold of the CFL condition.
Convolutional Autoencoders, Clustering and POD for Low-dimensional Parametrization of Navier-Stokes Equations
Simulations of large-scale dynamical systems require expensive computations.
Learning finite difference methods for reaction-diffusion type equations with FCNN
To solve the second problem, a network architecture can be built as a framework to learn a finite difference method.
Disturbing Target Values for Neural Network Regularization
This active regularization makes use of the model behavior during training to regularize it in a more directed manner.
