Hub

Browse all posts in the Hub category.

13 to 18 of 19

23JUL 2022
Hub
Lloyd’s Algorithm
Author: Martín Hernández, FAU DCN-AvH Code: In this repository, we show a code for Lloyd’s algorithm. Also called Voronoid iteration, this is an iterative algorithm finding for equispaced convex cells in euclidean space. Lloyd’s algorithm finds the distribution of the cells computing their center of mass and iteratively applying the Voronoid tessellation. Like the closely […]
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20JUN 2022
Hub
Robust neural ODEs
The code implements the gradient regularization method of robust training in the setting of neural ODEs. Various jupyter notebooks are included that generate plots comparing standard to robust training for 2d point clouds. Code: A good starting point is robustness_plots.ipynb Code is based on GitHub: borjanG : 2021-dynamical-systems that uses the torchdiffeq package GitHub : […]
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07APR 2022
Hub
The interplay of control and deep learning
The interplay of control and Deep Learning It is superﬂuous to state the impact deep (machine) learning has had on modern technology, as it powers many tools of modern society, ranging from web searches to content ﬁltering on social networks. It is also increasingly present in consumer products such as cameras, smartphones and automobiles. […]
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03JAN 2022
EZuazua
pyGasControls library (simulation software)
Martin Gugat, Enrique Zuazua, Aleksey Sikstel, FAU DCN-AvH Code: [HINT] To run the software on your computer, you may have to install additional standard software packages (like cmake and a c++ compiler) and additional libraries (lapack, PETSc). In order to optimize the operation of gas transportation networks, as a first step a powerful […]
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16DEC 2021
Hub
Hamilton-Jacobi Equations: Inverse Design
Carlos Esteve, Deusto CCM Code: In a previous post “Inverse Design For Hamilton-Jacobi Equations“, described all the possible initial states that agree with the given observation of the system at time on the reconstruction of the initial state in many evolution models. Our goal here is to study the inverse design problem associated to Hamilton […]
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14DEC 2021
Hub
Random Batch Methods for Linear-Quadratic Optimal Control Problems
Daniel Veldman, FAU DCN-AvH Code: || Also available @Daniël’s GitHub In a previous post “Randomized time-splitting in linear-quadratic optimal control“, it was proposed to use the Random Batch Method (RBM) to solve classical Linear-Quadratic (LQ) optimal control problems. This contribution is concerned with the corresponding numerical implementation. We thus consider the classical LQ optimal control […]Daniel Veldman, FAU DCN-AvH Code: || Also available @Daniël’s GitHub In a previous post “Randomized time-splitting in linear-quadratic optimal control“, it was proposed to use the Random Batch Method (RBM) to solve classical Linear-Quadratic (LQ) optimal control problems. This contribution is concerned with the corresponding numerical implementation. We thus consider the classical LQ optimal control […]
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