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Deep Learning Models for Global Coordinate Transformations that Linearize PDEs
We develop a deep autoencoder architecture that can be used to find a co...
11/07/2019 ∙ by Craig Gin, et al. ∙
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Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models
Rapid simulations of advection-dominated problems are vital for multiple...
09/18/2019 ∙ by Romit Maulik, et al. ∙
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Deep learning for universal linear embeddings of nonlinear dynamics
Identifying coordinate transformations that make strongly nonlinear dyna...
12/27/2017 ∙ by Bethany Lusch, et al. ∙
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Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Libraries
We consider N-way data arrays and low-rank tensor factorizations where t...
08/16/2016 ∙ by Bethany Lusch, et al. ∙
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Submodular Hamming Metrics
We show that there is a largely unexplored class of functions (positive ...
11/06/2015 ∙ by Jennifer Gillenwater, et al. ∙
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