Reduced order modelling techniques for OpenFOAM
-
Updated
Jul 19, 2024 - C++
Reduced order modelling techniques for OpenFOAM
Model reduction library with an emphasis on large scale parallelism and linear subspace methods
FlowNet - Data-Driven Reservoir Predictions
Data-driven reduced order modeling for nonlinear dynamical systems
The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN
Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) Network
Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10915-021-01462-7.
Probabilistic Response mOdel Fitting with Interactive Tools
core C++ library
ITHACA-SEM - In real Time Highly Advanced Computational Applications for Spectral Element Methods
Create reduced-order state-space models for lithium-ion batteries utilising realisation algorithms.
Standardized Non-Intrusive Reduced Order Modeling
Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.
The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-uniform parameter/time-varying grid, such that the Kolmogorov n-width of the mapped data on the learned grid is minimized.
AI4Science: Efficient data-driven Online Model Learning (OML) / system identification and control
Python Framework for data-driven model Order Reduction of multi-physiCs problEms
RBniCSx - reduced order modelling in FEniCSx
For full documentation, see:
Matlab implementation of online and window dynamic mode decomposition algorithms
Add a description, image, and links to the reduced-order-models topic page so that developers can more easily learn about it.
To associate your repository with the reduced-order-models topic, visit your repo's landing page and select "manage topics."