Model reduction library with an emphasis on large scale parallelism and linear subspace methods
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Updated
Aug 21, 2024 - C++
Model reduction library with an emphasis on large scale parallelism and linear subspace methods
Reduced order modelling techniques for OpenFOAM
FlowNet - Data-Driven Reservoir Predictions
The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN
Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) Network
core C++ library
Data-driven reduced order modeling for nonlinear dynamical systems
Create reduced-order state-space models for lithium-ion batteries utilising realisation algorithms.
AI4Science: Efficient data-driven Online Model Learning (OML) / system identification and control
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.
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.
ITHACA-SEM - In real Time Highly Advanced Computational Applications for Spectral Element Methods
Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.
Probabilistic Response mOdel Fitting with Interactive Tools
Matlab implementation of online and window dynamic mode decomposition algorithms
Standardized Non-Intrusive Reduced Order Modeling
A straightforward prototype implementation of the incremental POD to demonstrate its use in reduced order modeling.
RBniCSx - reduced order modelling in FEniCSx
Python Framework for data-driven model Order Reduction of multi-physiCs problEms
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