rmojgani
■ scientific machine learning (SciML) ■ machine learning in dynamical systems ■ computational sciences ■ data-driven modeling ■ model reduction
Pinned Repositories
MEDIDA
MEDIDA: Model Error Discovery with Interpretability and Data Assimilation
ALRTFJ-CUG17
CFD_AUT
Project I : A control-volume-based finite element method is used to solve the in compressible flow, in a lid-driven cavity. Project II : Roe's Riemann solver is used for compressible Euler equations on unstructured grids, flow on an airfoil.
Cross-EOF-Eddy-Feedback-Model
This repository contains scripts/codes to calculate cross-EOF eddy-zonal flow feedbacks of the annular modes based on NCL
LPINNs
To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary conditions
MEDIDA_QG
NPE-and-NTK
PhysicsAwareAE
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.
registration_for_ALE
RLonKorali
rmojgani's Repositories
rmojgani/LPINNs
To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary conditions
rmojgani/PhysicsAwareAE
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.
rmojgani/CFD_AUT
Project I : A control-volume-based finite element method is used to solve the in compressible flow, in a lid-driven cavity. Project II : Roe's Riemann solver is used for compressible Euler equations on unstructured grids, flow on an airfoil.
rmojgani/MEDIDA_QG
rmojgani/NPE-and-NTK
rmojgani/registration_for_ALE
rmojgani/RLonKorali
rmojgani/ALRTFJ-CUG17
rmojgani/Cross-EOF-Eddy-Feedback-Model
This repository contains scripts/codes to calculate cross-EOF eddy-zonal flow feedbacks of the annular modes based on NCL
rmojgani/DeepLearningSWE
rmojgani/deepxde
Deep learning library for solving differential equations and more
rmojgani/delete
rmojgani/dominant-balance
Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)
rmojgani/ExGAN
Adversarial Generation of Extreme Samples
rmojgani/korali
High-performance framework for uncertainty quantification, optimization and reinforcement learning.
rmojgani/meta-pde
rmojgani/nmor
Deep learning framework for model reduction of dynamical systems
rmojgani/PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
rmojgani/psiqg
rmojgani/PySR
High-Performance Symbolic Regression in Python and Julia
rmojgani/RCESN_spatio_temporal
Spatio-temporal forecasting of Lorenz96 with RC-ESN, RNN-LSTM and ANN
rmojgani/resume-template
:page_facing_up::briefcase::tophat: A simple Jekyll + GitHub Pages powered resume template.
rmojgani/rmojgani
rmojgani/rmojgani.github.io
rmojgani/RNN-Lyapunov-Spectrum
A data-driven method to calculate the Lyapunov exponent of a dynamical system employing a GRU-RNN.
rmojgani/rom-operator-inference-Python3
Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.
rmojgani/ROM-OpInf-Combustion-2D
Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" by S. A. McQuarrie, C. Huang, and K. E. Willcox.
rmojgani/rvm-find
Relevance Vector Machines (RVMs) for Bayesian data-driven discovery of PDEs.
rmojgani/SparseCollocationJHB
rmojgani/wave_decomposition