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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.

2017_lyu_sphericalstructuredfeatures

2022_tanaka_hamiltoniangp_noisysparsedata

This repository implements the 2022 paper 'Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data' by Tanaka et al.

2024_hamiltonian_dynamics

2d-fluid-simulator

2D incompressible fluid solver implemented in Taichi.

afc-june-demos

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

black_oil_flow_simulator

Black Oil Equation Flow Simulator repository for comprehensive single-phase, two-phase, and three-phase fluid flow simulations, designed for researchers, engineers, and students in fluid dynamics and reservoir engineering.

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

mce-vae

Codebase for MCE-VAE project

nvkm

Python + Jax implementation of the nonparametric Volterra kernels model

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.

opm-data

Clean version of opm-data without reference results

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.

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.

rfarell.github.io

ssgp

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.