Popular GitHub Repositories for Kde Plasma

OpenDILab RL Object Store

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

PPPL deep learning disruption prediction package

Quickly manage/control Ollama from plasma applet.

Tokamak plasma operation control through multi-objective reinforcement learning in KSTAR

A Python package for plasma physics PDEs.

OneTrainer docker images for use in GPU cloud and local environments. Includes AI-Dock KDE Plasma desktop with GPU acceleration and audio for authentication and improved user experience.

⚛️ ML in fusion industry/science ?⚡

CodeT5 LLM fine-tuned with C++ code from KDE

PyTorch implementation of Neural Netowrk Differential Equation Plasma Equilibrium Solver.

Research-repository: Disruption Prediction and Analysis through Multimodal Deep Learning in KSTAR

Research-repository: Disruption Prediction and Analysis through Multimodal Deep Learning in KSTAR

Custom fedora / ublue image built with nix package manager support integrated out of the box.

Project focused on optimizing artificial neural network (ANN) algorithms to generalize from experimental data describing the epitaxial synthesis of thin film semiconductor crystals. Incentivized by a project assignment in a CS 5300 - Artificial Neural Systems course taken at Western Michigan University.

?️ Automatic KDE Plasma memory leak prevention and system optimization. The first comprehensive solution for KDE memory management, preventing system freezes and performance issues caused by plasmashell and kglobalacceld memory leaks.

An exploration of anomaly detection for insider risk implemented by KDE, Minhash, and K-Means, based on PySpark and Colab.

Code for the paper "An Empirical Analysis of KDE-based Generative Models on Small Datasets"

? Credit Risk Prediction ? A machine-learning–based analysis designed to predict whether a loan applicant is likely to default. Using a refined Credit Risk Dataset, I cleaned, processed, and visualized key financial features such as income, loan amount, and credit history. Multiple classification models were trained with accuracy.

PyPIC3D is a 3D particle-in-cell code written in Python using Jax.

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