Python Framework for data-driven model Order Reduction of multi-physiCs problEms
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Updated
Jun 3, 2024 - Jupyter Notebook
Python Framework for data-driven model Order Reduction of multi-physiCs problEms
Data-driven reduced order modeling for nonlinear dynamical systems
Model reduction library with an emphasis on large scale parallelism and linear subspace methods
RBniCSx - reduced order modelling in FEniCSx
The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN
Reduced order modelling techniques for OpenFOAM
Numerical Methods for Vlasov-Poisson and Vlasov-Maxwell Systems
Optimization of Pressure Management Strategies for Geological CO2 Sequestration Using Surrogate Model-based Reinforcement Learning
Probabilistic Response mOdel Fitting with Interactive Tools
A GPU-accelerated toolbox for hyperbolic PDEs in a weaker (viscosity) sense. It leverages the integral to the solution of the conservation of momentum problem (being equivalent to the derivative of Hamilton-Jacobi equations) in one spatial dimension. We resolve such hyperbolic differential equations using wave-front propagating schemes on a spat…
core C++ library
Non-intrusive Reduced Order Modeling package
Delay Embedded Regressive Reduced Order Model
Create reduced-order state-space models for lithium-ion batteries utilising realisation algorithms.
A straightforward prototype implementation of the incremental POD to demonstrate its use in reduced order modeling.
It is a pdf generating application. It takes data from users and generates a pdf.
Repository for my thesis on "A Comparison of Reduced-Order Models for Wing Buffet Predictions"
Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) Network
Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.
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.
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