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Structure-preserving approaches and data-driven closure modeling for model order reduction

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Publications associées à Structure-preserving approaches and data-driven closure modeling for model order reduction | EPFL Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Structure-preserving approaches and data-driven closure modeling for model order reduction

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Chattez avec Graph Search

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

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Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Anomalous dissipation and other non-smooth phenomena in fluids

Machine Learning for Modeling Stock Returns

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

SPACE-TIME REDUCED BASIS METHODS FOR PARAMETRIZED UNSTEADY STOKES EQUATIONS

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks