The official Open-Asset-Importer-Library Repository. Loads 40+ 3D-file-formats into one unified and clean data structure.
-
Updated
May 23, 2024 - C++
The official Open-Asset-Importer-Library Repository. Loads 40+ 3D-file-formats into one unified and clean data structure.
Android OpenGL 2.0 application to view 3D models. Published on Play Store
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Tensorflow implementation of variational auto-encoder for MNIST
Chnroutes rules for routers、Shadowrocket、Quantumult、acl、v2rayNG、v2rayN、pac、Qv2ray、NekoRay、Nekobox、v2rayA、dae、RouterOS、sing-box、v2ray config file.
eBPF-based Linux high-performance transparent proxy solution.
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
The Base interface of the SciML ecosystem
Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
A WebGL based BIM viewer, built on three.js and Vue. Used to view gltf, ifc, obj, dae, stl models, etc.
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
🌐 Fast 3D file format converter in C++ supporting OBJ, 3DS, MA, MB, XSI, LWO, DXF, STL, MAT, DAE.
A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Add a description, image, and links to the dae topic page so that developers can more easily learn about it.
To associate your repository with the dae topic, visit your repo's landing page and select "manage topics."