Search Results for "as built"

This package provides a low level interface for PETSc and allows combining julia features (such as automatic differentiation) with the PETSc infrastructure and nonlinear solvers.

Width-sensitive formatter for Julia code. Inspired by gofmt, refmt, black, and prettier. Built with CSTParser. Sane defaults out of the box with options to customize. Supports YAS, Blue and SciML style guides. JuliaFormatter.toml configuration file to store options. JuliaFormatter exports format, format_file, format_text, and format_md. format_md has the same API as format_text but differ in that format_md expects the text content to be a Markdown document.

...It exposes simple commands to format either a selected range or an entire buffer, and integrates cleanly into everyday editing workflows. The plugin ships with a registry of built-in formatters and a pluggable architecture, allowing other plugins to register additional formatters without friction. Configuration is handled through maktaba and Glaive flags, so you can choose per-filetype tools, pass custom options, or point to specific formatter executables. Autoformat can be enabled via standard Vim autocommands, making it easy to format on filetype or on write while still allowing opt-out on a per-buffer basis. ...

Create beautiful, analytic applications in Julia. Built on top of Plotly.js, React and HTTP.jl, Dash ties modern UI elements like dropdowns, sliders, and graphs directly to your analytical Julia code.

...It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI Agent Host is built specifically for LangChain, a framework dedicated to developing applications powered by language models. LangChain recognizes that the most powerful and distinctive applications go beyond simply utilizing a language model and strive to be data-aware and agentic. Being data-aware involves connecting a language model to other sources of data, enabling a comprehensive understanding and analysis of information.

Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels...

In the selective laser melting process (SLM), components are built up layer by layer by incremental melting of metal powder with a laser beam. This process leads to locally inhomogeneous material properties of the manufactured components. By integrating these specific material properties of the SLM-process into a topology optimization, product developers can be supported in the design process by simulation.