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Learning in infinite dimension with neural operators.
TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks (UQPINNs).
TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).
Uncertainty Quantification in the POD-NN framework
Aiming at the dissertations nonstandard format problems such as chart format, writing format and formula format, a simple and easy-to-use LaTeX template for Hohai dissertations is provided. The tem…
(Minimalism Style) Powered by Jekyll, based on the Minimal Mistakes theme and Jason Ansel's website
Collection of advice for prospective and current PhD students
Curated list of awesome advice, tips, and resources to prepare for PhD/grad school.
Non-intrusive reduced order models using proper orthogonal decomposition (POD) and radial basis function (RBF) interpolation for shallow water equations
Python tools for non-intrusive reduced order modeling
Neural Ordinary Differential Equations for model order reduction of time-dependent PDEs
Python program for EOF analysis of MIKE 21 data to enable training of Sparse Gaussian Process models to predict flood inundation extent.
An implementation of HBV Hydrological model by C++.
C++ implementation of Reduced Basis Method (RBM) with Proper Orthogonal Decomposition (POD) and Discrete Empirical Interpolation Method (DEIM); see Buffoni & Willcox, AIAA 2010-5008 for more details
Matlab files with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz http:…
RBniCS - reduced order modelling in FEniCS (legacy)
A parallel C++ MPI code to calculates the proper orthogonal decomposition modes (aka principal components) from a series of observations / measurements.
A Python package for spectral proper orthogonal decomposition (SPOD).
Spectral proper orthogonal decomposition in Matlab
OpenPIV Proper Orthogonal Decomposition (POD) Matlab Toolbox
Deep-learning model for optimised proper orthogonal decomposition of non-linear, hyperbolic, parametric PDEs based on a pre-processing method of the full-order solutions
Implementation of the Finite Element Method (FEM) and the Proper Orthogonal Decomposition (POD) to study the heat equation
finite element based reduced order modelling-proper orthogonal decomposition of geometrically parametrized stokes flow
MPI-based parallel proper orthogonal decomposition (POD) in C++