Stars
Self-study on Larry Wasserman's "All of Statistics"
Implementation of SVRG and SAGA optimization algorithms for deep learning topics.
[NeurIPS 2023 Spotlight] Separable Physics-Informed Neural Networks
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
PyTorch implementation of AlphaZero Chess from scratch
Flatiron School Data Science Bootcamp Phase 1 Project.
Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawaguchi, G E Karniadakis, Locally adaptive activation functions …
Chess reinforcement learning by AlphaGo Zero methods.
Toolbox for converting analog to spiking neural networks (ANN to SNN), and running them in a spiking neuron simulator.
Collected study materials in Numerical Optimization ANU@MATH3514(HPC)
An easy to use PyTorch implementation of the Kolmogorov Arnold Network and a few novel variations
Kolmogorov-Arnold Networks (KAN) using Jacobi polynomials instead of B-splines.
Kolmogorov-Arnold Networks (KAN) using orthogonal polynomials instead of B-splines.
Kolmogorov-Arnold Networks (KAN) using Chebyshev polynomials instead of B-splines.
A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold N…
Deep Learning for Solving Differential Equations (Educational)
Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
Exploring the split-operator method.
GPUE-group / GPUE
Forked from mlxd/GPUEGPU Gross-Pitaevskii Equation numerical solver for Bose-Einstein condensates.
⚛️ A module for solving and visualizing the Schrödinger equation.