individual convolutional autoencoders (iCAEs) for low-dimensional parametrization
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Updated
Nov 24, 2022 - Python
individual convolutional autoencoders (iCAEs) for low-dimensional parametrization
Python code of the paper Model order reduction of deep structured state-space models: A system-theoretic approach
Implementation of balanced truncation method for semi-discretized heat equation, boundary control, Neumann observation. In 2018.
Frequency domain reduced order model (ROM) for second-order systems with frequency dependent matrices
Graph Feedforward Networks: a resolution-invariant generalisation of feedforward networks for graphical data, applied to model order reduction
Implementation of the Generalized Multiscale Finite Element Method (GMsFEM) for solution problems in heterogeneous media with finite volume approximation on the fine grid
Minimal rational interpolation (MRI) suite for time-harmonic problems in electromagnetism governed by Maxwell's equations.
This repository implements a new model order reduction approach for moving fronts.
Example of control simulations
Passivity-preserving model reduction for descriptor systems via spectral factorization
morgen - Model Order Reduction for Gas and Energy Networks
Frequency domain reduced order model (ROM) for second-order systems with frequency dependent matrices
Supporting code for "reduced order modeling using advection-aware autoencoders"
Python libraries (NumPy-based) to perform model order reduction, clustering and data analysis for combustion and aerothermochemistry data
Supplemental Material for "BUQEYE Guide to Projection-Based Emulators in Nuclear Physics"
morgen - Model Order Reduction for Gas and Energy Networks
RBniCS - reduced order modelling in FEniCS
PROTON - A Python Framework for Physics-Based Electromigration Assessment on Contemporary VLSI Power Grids
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