222 skills found · Page 5 of 8
orue / Git ConfigurationProduction-grade git configuration optimized for software engineering workflows
Bhavya / Gen AI PromptsA comprehensive collection of AI prompts designed for software engineers, engineering managers, product managers, and career development. These prompts are optimized for different AI models and organized by role and use case.
wo315 / AA222 CollectionsEngineering Design Optimization
codextech / Openclaw HandbookInternal engineering handbook for OpenClaw — architecture, configuration, agent orchestration, memory, skills, and cost optimization.
MastenSpace / PysurEngineering surrogate modeling and optimization toolkit.
jiaxiang-cheng / PyTorch PDQN For Digital Twin ACSPyTorch implementation of RIC for conveyor systems with Deep Q-Networks (DQN) and Profit-Sharing (PS). Wang, T., Cheng, J., Yang, Y., Esposito, C., Snoussi, H., & Tao, F. (2020). Adaptive Optimization Method in Digital Twin Conveyor Systems via Range-Inspection Control. IEEE Transactions on Automation Science and Engineering.
The-Swarm-Corporation / Awesome Automated Prompt EngineeringThis repository serves as a central hub for discovering tools and services focused on automated prompt engineering. Whether you're looking to optimize your prompts for generative AI models or enhance the capabilities of your agents, you'll find a wide range of resources here.
xiaotaonan / Algebra Topology Differential Calculus And Optimization Theory For Computer Science And Engineering宾夕法尼亚大学计算机和信息科学系教授 Jean Gallier 的开源书籍《代数，拓扑，微分，与计算机科学与工程的优化理论》
SajadAHMAD1 / CPSOCGSA For Engineering Design OptimizationConstriction Coefficient Based PSO and Chaotic GSA for Engineering Design Problems
malteos / Awesome Prompt OptimizationA curated collection of resources for prompt engineering, optimization, and automatic prompt generation across text, image, video, and multimodal AI systems.
tengjuilin / Intro Sci ComputingUW AMATH 301. Scientific computing and numerical methods for physical, biological, and engineering problems. Topics include root-finding, optimization, curve fitting, solving linear systems, singular value decomposition (SVD, PCA), numerical differentiation and integration, solving first-order and higher order ODEs, stability and stiffness of ODEs, phase portraits, chaotic systems, and Fourier transform.
UofR-ESI-Lab / Optimization TutorialThis workshop introduces basic concepts, models and algorithms in linear programming, convex optimization and stochastic optimization. A MATLAB-based modeling system for convex optimization, CVX, is covered. Case studies are presented including an production plan problem, smart electric vehicle charging, a newsvendor problem, and a regression model. The codes are provided for practice. The workshop is organized by IEEE South Sask section & PES/IAS Joint Chapter in collaboration with Engineering Graduate Student Association (EGSA) and the Faculty of Engineering and Applied Science at the University of Regina.
hoangsonww / DevVerse SWE Blog🪐 DevVerse CS Blog – A modern, high-performance blog app built with Next.js, Vercel, Supabase, TypeScript, MDX, and Framer Motion, featuring real-time search, dynamic routing, and optimized SEO for deep dives into computer science and software engineering topics. © Son Nguyen 2026.
aliasgharheidaricom / RUN Beyond The Metaphor An Efficient Optimization Algorithm Based On Runge Kutta MethodThe optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliché methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliché methods suffer from the locally efficient performance, biased verification methods on easy problems, and high similarity between their components' interactions. This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics. The proposed RUNge Kutta optimizer (RUN) was developed to deal with various types of optimization problems in the future. The RUN utilizes the logic of slope variations computed by the RK method as a promising and logical searching mechanism for global optimization. This search mechanism benefits from two active exploration and exploitation phases for exploring the promising regions in the feature space and constructive movement toward the global best solution. Furthermore, an enhanced solution quality (ESQ) mechanism is employed to avoid the local optimal solutions and increase convergence speed. The RUN algorithm's efficiency was evaluated by comparing with other metaheuristic algorithms in 50 mathematical test functions and four real-world engineering problems. The RUN provided very promising and competitive results, showing superior exploration and exploitation tendencies, fast convergence rate, and local optima avoidance. In optimizing the constrained engineering problems, the metaphor-free RUN demonstrated its suitable performance as well. The authors invite the community for extensive evaluations of this deep-rooted optimizer as a promising tool for real-world optimization. The source codes, supplementary materials, and guidance for the developed method will be publicly available at different hubs at http://aliasgharheidari.com/RUN.html.
MKcodeshere / Self Improving Text2SQLadaptive database query systems that fix their own mistakes and optimize performance automatically using Stanford's Agentic Context Engineering
YangYuSCU / DE PINNwith comprehensive numerical study on solving neutron diffusion eigenvalue problems) We present a data-enabled physics-informed neural network (DEPINN) with comprehensive numerical study for solving industrial scale neutron diffusion eigenvalue problems (NDEPs). In order to achieve an engineering acceptable accuracy for complex engineering problems, a very small amount of prior data from physical experiments are suggested to be used, to improve the accuracy and efficiency of training. We design an adaptive optimization procedure with Adam and LBFGS to accelerate the convergence in the training stage. We discuss the effect of different physical parameters, sampling techniques, loss function allocation and the generalization performance of the proposed DEPINN model for solving complex problem. The feasibility of proposed DEPINN model is tested on three typical benchmark problems, from simple geometry to complex geometry, and from mono-energetic equation to two-group equations. Numerous numerical results show that DEPINN can efficiently solve NDEPs with an appropriate optimization procedure. The proposed DEPINN can be generalized for other input parameter settings once its structure been trained. This work confirms the possibility of DEPINN for practical engineering applications in nuclear reactor physics.
hexart / Prompt BoosterPrompt Booster: A comprehensive tool for optimizing LLM prompts with version control, A/B testing, and template management. Supports multiple AI providers (OpenAI, Gemini, DeepSeek, Qwen, etc.) across web and desktop platforms. Increase your AI prompt effectiveness with professional engineering tools.
joshiji789 / PINN S For Heat Transfer ProblemIn recent years, the use of physics-informed neural networks (PINNs) has gained popularity across several engineering disciplines due to their effectiveness in solving linear and non-linear partial differential equations (PDE) and real-world problems despite noisy data. The basic approach used to solve the PINNs is to construct the neural network and define a loss as a function of PDE and boundary/initial conditions (B.C/I.C). To get the dependent variable from the PDE, our aim is to minimize the loss function formed from fundamental equations by employing effective optimization techniques.
wangyahgui / PDGANThis project first presents a coherent signal demodulation method based on generative adversarial networks (GAN), called a phase demodulation generative adversarial network (PDGAN). We applyed GAN method to the field of Doppler signal demodulation for laser voice detection.Demodulation of the Doppler signal from a coherent signal is accomplished through unsupervised learning within the PDGAN, layer by layer, with global supervised feedback learning for fine-tuning. Drawing upon the adversarial principle of GAN, we let the coherent signal, Z, serve as an input for G and let the generated demodulated Doppler signal G(Z) and the clean Doppler signal X serve as the input for D. By means of alternate training and optimization, G learns the mapping relationship between the coherent signal, Z, and the Doppler signal, X, thereby achieving the goal of demodulating the coherent signal. This project mainly includes related data sets of Doppler signal and corresponding coherent signal. The structure and detailed description of the network are planned to be published in the Journal of optical engineering. The author can also be contacted for information wangyahui@aoe.ac.cn
peterdsharpe / Phd Thesis"Accelerating Practical Engineering Design Optimization with Computational Graph Transformations"
