聚变科学与工程资源库
检索物理代码、实验数据、代理模型、工程几何、技术文档与验证资料，并核对来源、版本和使用条件。
Tamu23/Forecasting-Plasma-Instability-in-Fusion-Simulations
Tamu23Using the 1-dimensional fusion reaction model created by Dr. Ken Owens, our task is to predict instability in the system, preferably enough time before the instability occurs in order to take appropriate preventative action.
Optimal lift movement based on rest prediction
Dayananda Sagar College of EngineeringExisting elevator control systems in office buildings primarily rely on reactive scheduling strategies that respond only after passenger requests occur, leading to increased waiting times during peak traffic periods. Although reinforcement learning (RL) and deep learning approaches have been explored for intelligent elevator control, many existing methods require high computational complexity and large training datasets, limiting their suitability for embedded elevator controllers and practical smart-building deployment. To address this gap, this paper proposes a lightweight predictive elevator control framework based on the eXtreme gradient boosting (XGBoost) machine learning algorithm for rest-floor prediction. The proposed method uses historical traffic patterns and temporal features to predict future demand floors and proactively reposition idle elevators before passenger requests occur. A comprehensive simulation was conducted for multiple office-building configurations with varying numbers of floors and elevators over one year of operation using realistic traffic patterns. The proposed predictive strategy was compared with a conventional reactive control approach. Results show that the proposed framework reduces cumulative passenger waiting time by approximately 11%–22%, with larger improvements observed in high-rise and high-traffic scenarios, while maintaining comparable energy consumption. The study demonstrates that lightweight supervised machine learning can provide an effective and computationally efficient solution for predictive elevator control in embedded smart-building systems.
LuisDavidRuizOrtiz/fusion-materials-geant4
LuisDavidRuizOrtizGeant4-based simulation framework for evaluating advanced plasma-facing materials and blanket configurations in fusion reactors. Developed as part of a master's thesis project.
SCOPE-NO research artifact for runtime adequacy testing of a reusable two-response learned spectral-operator library
Gyeongnam Intelligence Innovation Center (GIIC), Kyungnam University, 7, Gyeongnamdaehak-ro, Masanhappo-gu, Changwon 51767, Republic of KoreaResearch artifact for the manuscript Runtime Adequacy Testing for a Reusable Two-Response Learned Spectral-Operator Library: A Prospective Synthetic Validity Map. The deposit contains hash-verified code, frozen configurations, synthetic blinded scores and labels, unlocked analysis rows, calibration states, model checkpoints, metrics, audits, and CUDA runtime measurements for the completed primary prospective confirmation study and a separately frozen prospective follow-up evaluation. The follow-up was designed after the primary study closed and frozen before its own scoring; it is binding only within its second-stage protocol. The archive preserves failed and withdrawn gates. The primary-study evidence supports exact score-row-to-result reproduction but not complete simulation-to-score regeneration for every arm because historical raw calibration retention is incomplete. Original SCOPE-NO code is MIT licensed. Synthetic data, labels, metrics, documentation, and research reports are CC BY 4.0 licensed. Bundled third-party material retains its upstream terms.
29syliu/cuGMEC
29syliuA High-Performance Code for Gyrokinetic-MHD Hybrid Simulation on GPUs with CUDA C++
rande-code/TL-RL-FusionNet-Dataset
rande-codeCompanion dataset for TL-RL-FusionNet: 1,000 Windows behavioral samples (500 ransomware, 500 benign) with 100 Cuckoo Sandbox features across 25 ransomware families for machine learning-based ransomware detection.
ENTRE A TRADIÇÃO E A TRANSFORMAÇÃO: CULTURA ORGANIZACIONAL, MODELOS MENTAIS E RESISTÊNCIA À MUDANÇA NOS CORREIOS BRASILEIROS
ZenodoEste artigo analisa a relação entre cultura organizacional, modelos mentais e resistência à mudança nos Correios, considerando a tensão entre o imaginário construído pelos atores organizacionais e as transformações requeridas pelo setor postal contemporâneo. O estudo parte da questão de como os elementos simbólicos presentes na percepção dos empregados se relacionam com os desafios de modernização e adaptação da empresa ao ambiente competitivo e tecnológico. Trata-se de pesquisa exploratória, de abordagem qualitativa, desenvolvida mediante estudo de caso e análise de conteúdo. Foram examinadas aproximadamente quatro mil respostas coletadas entre 2023 e 2025, nas quais empregados dos diferentes níveis hierárquicos foram convidados a representar os Correios por meio de cinco palavras-chave. Os dados foram sistematizados e codificados com o auxílio do software MaxQDA e interpretados à luz das contribuições sobre cultura organizacional, modelos mentais e prisões psíquicas. Os resultados evidenciam a predominância de representações associadas a orgulho, essencialidade, confiança, tradição, excelência, grandeza, família, soberania e história, revelando um imaginário fortemente vinculado à trajetória e à relevância histórica da instituição. Em contraste, elementos relacionados à inovação, agilidade, tecnologia, competitividade e adaptação aparecem de forma pouco expressiva. Conclui-se que essa configuração simbólica pode constituir um fator de rigidez cultural e dificultar a construção de novos referenciais para a transformação organizacional, sobretudo diante das mudanças estruturais do mercado postal e logístico. O estudo aponta, portanto, para a necessidade de articular preservação da identidade institucional e renovação dos modelos mentais que orientam a organização.
YuguangTong/Fourier-Laplace-plasma-response-in-driven-Gyrokinetics
YuguangTongAnalytic/numeric solution of plasma response to antenna driving in gyro kinetic system. Comparison is made with simulation by AstroGK.
Source code for MSMOA
Southwest University of Finance and Economics, Financial Office, Chengdu 611130, ChinaThis repository provides the source code, experimental scripts, configuration files, benchmark-function definitions, application-case data, plotting scripts, and statistical-testing scripts used to support the manuscript entitled “Performance of Multi-strategy Optimized Mayfly Optimization Algorithm for Location Selection of University Reimbursement Form Submission Terminals.” The repository is designed to support reproducibility of the main experiments reported in the manuscript. It includes MATLAB implementations of the proposed Multi-strategy Optimized Mayfly Optimization Algorithm (MSMOA) and the compared algorithms, including MOA, PSO, GWO, SSA, and DESMA. The experimental scripts reproduce the main benchmark comparison, ablation study, parameter sensitivity analysis, Wilcoxon rank-sum tests, and the reimbursement-terminal location-selection application case. The benchmark experiments cover scalable test functions evaluated under 30D, 50D, and 100D settings, as well as fixed-dimensional benchmark functions. The plotting scripts generate the convergence curves and application-case figures, while the table-generation scripts export the numerical results used in the manuscript. The application case uses anonymized or simulated demand-point coordinates and demand weights to illustrate a simplified coordinate-distance-based facility-location model. No personally identifiable information, human participant data, questionnaire data, interview data, or individual-level behavioral data are included. A README file is provided to describe the folder structure, software environment, execution steps, and expected outputs. The repository is intended to facilitate independent verification, reuse, and extension of the proposed MSMOA framework.
xingliuUT/SKiM
xingliuUTRoot-finding code (Muller's method) to solve the dispersion relation of the gyrokinetic equation for plasma
mfisher35/fusion
mfisher35scripts for plasma simulations
coderunner-framework/gs2crmod
coderunner-frameworkGS2 is a gyrokinetic flux tube initial value turbulence code which can be used for fusion or astrophysical plasmas. CodeRunner is a framework for the automated running and analysis of large simulations. This module allows GS2 (and its sister code AstroGK) to harness the power of the CodeRunner framework.
jiffies64/limn
jiffies64A deep learning framework built to be read: lazy tensors, reverse-mode autograd, kernel fusion, a printable IR, conv layers, dtypes from int8 to float64, and JIT backends for C and CUDA.
saurabh-1907/multimodal-ad-moderation
saurabh-1907Multimodal ad-creative policy classification: CLIP + XLM-R fusion with OCR, PR-AUC/calibration-first evaluation and a threshold policy tied to human-review cost
enidfresh519/exist2026-ordantis
enidfresh519Detect sexism in memes using Gemini-Enriched Multimodal Fusion. Rank-leading system for the EXIST 2026 task on sexism characterization.
dtold/PyGKDis
dtoldGyrokinetic dispersion solver for homogeneous plasmas
SolshineCode/Nuclear-Fusion-Beam-to-Target-PIC-example
SolshineCodeSimple educational model demonstrating beam-to-target fusion concepts using particle tensors. Models (as a concept, not accurately) deuterium beam impacting tritium target. This is inspired from some recent discussions about my past work with beam to target fusion reactors and the usefulness of tensor-based plasma simulations.
cchandre/Guiding-Center
cchandreGuiding-center dynamics in plasma physics