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1 to 8 of 8 Results
Replication Code for: Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems
May 22, 2024
Herkert, Robin, 2024, "Replication Code for: Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems", https://doi.org/10.18419/darus-4185, DaRUS, V1
This dataset includes the code to reproduce the results from the paper titled "Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems". In this paper randomized symplectic basis generation techniques are introduced. The numerical experiments where e...
Replication Data for: Constraint-aware neural networks for Riemann problems
Jan 11, 2024
Magiera, Jim M., 2024, "Replication Data for: Constraint-aware neural networks for Riemann problems", https://doi.org/10.18419/darus-3869, DaRUS, V1
Data sets of the article "Constraint-aware neural networks for Riemann problems", consisting of training and test data sets for Riemann solutions of the cubic flux model, an isothermal two-phase model, and the Euler equations for an ideal gas. You can find detailed information in...
Softwarepackage CCMOR2
Dec 19, 2023
Rettberg, Johannes; Wittwar, Dominik; Herkert, Robin, 2023, "Softwarepackage CCMOR2", https://doi.org/10.18419/darus-3839, DaRUS, V1
The dataset entails the code of the CCMOR2 package developed in Matlab. This project aims to model and perform certified model order reduction on multi-physical systems. The systems are formulated in the port-Hamiltonian framework which incorporates useful system theoretic proper...
Replication Code for: Randomized Symplectic Model Order Reduction for Hamiltonian Systems
Jun 6, 2023
Herkert, Robin, 2023, "Replication Code for: Randomized Symplectic Model Order Reduction for Hamiltonian Systems", https://doi.org/10.18419/darus-3519, DaRUS, V1
This dataset includes the code to reproduce the results from the paper titled "Randomized Symplectic Model Order Reduction for Hamiltonian Systems". In this paper randomized symplectic basis generation techniques are introduced. The numerical experiments where error decay rates a...
Interface Preserving Moving Mesh (Code)
Interface Preserving Moving Mesh (Code)
Mar 8, 2023
Alkämper, Maria; Magiera, Jim M., 2022, "Interface Preserving Moving Mesh (Code)", https://doi.org/10.18419/darus-1671, DaRUS, V2
Open source implementation in C++ for an interface preserving moving mesh in 2d and 3d using CGAL Delaunay triangulations. The time-dependent computational mesh allows for large point deformations while preserving a lower dimensional interface surface. See README.md for more info...
Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence
Oct 26, 2022 - Modeling Strategies for Gas migration in Subsurface
Banerjee, Ishani; Walter, Peter, 2022, "Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence", https://doi.org/10.18419/darus-2815, DaRUS, V1
This dataset contains the codes used for implementing the method of forced probabilities of the manuscript: The Method of Forced Probabilities: A Computation Trick for Bayesian Model Evidence. Here, one can find the codes of implementation of the trick on stochastic invasion perc...
Modeling Strategies for Gas migration in Subsurface (IWS, Universität Stuttgart)
Mar 12, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3)
This dataverse contains modeling data (percolation type) and comparison methods (codes) for the project models for gas migration in porous media.
Stochastic Simulation and Safety Research for Hydrosystems (LS3)(Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart)
Mar 12, 2021
This dataverse contains data associated with the projects (as sub-dataverses) of LS3.
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