autodiff
geodesic_raytracing | autodiff | |
---|---|---|
18 | 7 | |
78 | 1,534 | |
- | 1.0% | |
9.6 | 7.8 | |
2 months ago | 24 days ago | |
C++ | C++ | |
MIT License | MIT License |
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geodesic_raytracing
- GPU accelerated raytracer that can render any analytic metric tensor
- Introducing posh: Type-Safe Graphics Programming in Rust
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Cyberpunk 2077: Technology Preview Of New Ray Tracing: Overdrive Mode Arrives April 11th
Someone has written a geodesic ray tracer in C++.
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A cool little blackhole simulation using raytracing and gravity simulation.
If you're ever interested in building GR sims, I've got a lot of experience here and maintain a similar tool for GR raytracing here
- Researchers suggest that wormholes may look almost identical to black holes
- Astray: A performance-portable geodesic ray tracing library.
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Clang for Windows
This is untrue, I've shipped single executable binaries before with mingw. If you check out the latest project I've released, the only binary dependencies are libOpenCL.dll and the steam dll
- New C++ features in GCC 12
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My GPU-accelerated raytracing renderer
I did build an implementation for a lot of this, so if you want this is probably a reasonable reference
- Best way to simulate total time dilation across a region of space.
autodiff
- The Elements of Differentiable Programming
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Astray: A performance-portable geodesic ray tracing library.
I completely agree. Specifying the metric rather than the Christoffel symbols would make it much easier for the users. Something like https://github.com/autodiff/autodiff might just work as the metric tensor is made up of primitives.
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Point-to-Point Distance Constraint: Gradient of Forward Kinematics
Old username :D So far I have been using Eigen for linear algebra and NLOPT for optimization algorithms. I have found "autodiff" that hopefully looks easy to use: https://github.com/autodiff/autodiff
- Autodiff: Simple C++17 library for Automatic Differentiation
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Gradients Without Backpropagation
Forward-mode differentiation is easy to implement in C++ with templates, operator overloading, and dual numbers (https://en.wikipedia.org/wiki/Automatic_differentiation#Auto...). Some libraries such as autodiff (https://github.com/autodiff/autodiff) and CppAD (https://github.com/coin-or/CppAD) use this method.
- Ensmallen: A C++ Library for Efficient Numerical Optimization
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I am creating a fast, header-only, C++ library for control algorithms
I was thinking of adding [autodiff](https://github.com/autodiff/autodiff) in the future, mainly because it works seamlessly with *Eigen*. One big advantage would be that I could use it for AD for NonlinearSystems as well.
What are some alternatives?
cuda-samples - Samples for CUDA Developers which demonstrates features in CUDA Toolkit
CppAD - A C++ Algorithmic Differentiation Package: Home Page
Drogon-torch-serve - Serve pytorch / torch models using Drogon
FastAD - FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.
MathNet - Math.NET Numerics
astray - A performance-portable geodesic ray tracing library.
MKL.NET - A simple cross platform .NET API for Intel MKL
papers - ISO/IEC JTC1 SC22 WG21 paper scheduling and management
CppRobotics - Header-only C++ library for robotics, control, and path planning algorithms. Work in progress, contributions are welcome!
AECforWebAssembly - A port of ArithmeticExpressionCompiler from x86 to WebAssembly, so that the programs written in the language can run in a browser. The compiler has been rewritten from JavaScript into C++.
PythonRobotics - Python sample codes for robotics algorithms.