rfarell's repositories
Reduced-Order-Modeling-Tutorials
A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Easily runnable on Google Colab.
2007_RahimiRecht_RandomFeaturesKernel
'Random Features for Large-Scale Kernel Machines' by Ali Rahimi and Benjamin Recht. The code within provides a detailed walkthrough of the techniques introduced in the paper, demonstrating how to efficiently implement large-scale kernel machines using random features for dimensionality reduction in high-dimensional data.
2d-fluid-simulator
2D incompressible fluid solver implemented in Taichi.
awesome-deep-learning-papers
The most cited deep learning papers
awesome-quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
basic-variogram-streamlit-application
Basic variogram application in streamlit
ContinualGP
Continual Gaussian Processes
Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
DLVKL
Deep latent-variable kernel learning
gprn
Gaussian Process Regression Network code
MCE-VAE
Codebase for MCE-VAE project
ODE2VAE
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
openingtree
Consolidated view of all your chess games from chess.com, lichess, grandmaster games or custom pgn.
PDEsNumericalMethods
Interactive Jupyter notebooks for solving PDEs using numerical methods. Features tutorials on the heat and wave equations with the Newton-Raphson method. Ideal for learning and teaching applied mathematics.
Single_Phase_Flow_Simulator
A comprehensive Python-based simulator for single-phase flow dynamics in reservoir engineering, implementing the black oil model equations.
Subsurface-Modeling-Tutorials
A comprehensive set of Jupyter notebooks covering subsurface modeling techniques, from single-phase to multi-phase flow, history matching, and usage of the Open Porous Media Flow Simulator. Compatible with Google Colab.