Rambod Mojgani's repositories
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
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.
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
dominant-balance
Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)
korali
High-performance framework for uncertainty quantification, optimization and reinforcement learning.
PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
PySR
High-Performance Symbolic Regression in Python and Julia
RCESN_spatio_temporal
Spatio-temporal forecasting of Lorenz96 with RC-ESN, RNN-LSTM and ANN
resume-template
:page_facing_up::briefcase::tophat: A simple Jekyll + GitHub Pages powered resume template.
RNN-Lyapunov-Spectrum
A data-driven method to calculate the Lyapunov exponent of a dynamical system employing a GRU-RNN.
rom-operator-inference-Python3
Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.
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.