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Found 3 papers, 2 papers with code
AI-enhanced iterative solvers for accelerating the solution of large scale parametrized systems
Recent advances in the field of machine learning open a new era in high performance computing.
Interval and fuzzy physics-informed neural networks for uncertain fields
Partial differential equations involving fuzzy and interval fields are traditionally solved using the finite element method where the input fields are sampled using some basis function expansion methods.
Non-intrusive Surrogate Modeling for Parametrized Time-dependent PDEs using Convolutional Autoencoders
Using the latent representation given by the encoder, a feed-forward neural network is efficiently trained to map points from the problem's parametric space to the compressed version of the respective solution matrices.
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