[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71098":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":34,"readmeContent":35,"aiSummary":36,"trendingCount":16,"starSnapshotCount":16,"syncStatus":37,"lastSyncTime":38,"discoverSource":39},71098,"warp","NVIDIA\u002Fwarp","NVIDIA","A Python framework for GPU-accelerated simulation, robotics, and machine learning.","https:\u002F\u002Fnvidia.github.io\u002Fwarp\u002F",null,"Python",6746,524,53,209,0,13,32,136,39,39.16,"Apache License 2.0",false,"main",true,[27,28,29,30,31,32,33],"cuda","differentiable-programming","gpu","gpu-acceleration","nvidia","nvidia-warp","python","2026-06-12 02:02:47","[![PyPI version](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fwarp-lang.svg)](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fwarp-lang)\n[![License](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-Apache_2.0-blue.svg)](https:\u002F\u002Fopensource.org\u002Flicenses\u002FApache-2.0)\n![GitHub commit activity](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcommit-activity\u002Fm\u002FNVIDIA\u002Fwarp?link=https%3A%2F%2Fgithub.com%2FNVIDIA%2Fwarp%2Fcommits%2Fmain)\n[![Downloads](https:\u002F\u002Fstatic.pepy.tech\u002Fbadge\u002Fwarp-lang\u002Fmonth)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Fwarp-lang)\n[![codecov](https:\u002F\u002Fcodecov.io\u002Fgithub\u002FNVIDIA\u002Fwarp\u002Fgraph\u002Fbadge.svg?token=7O1KSM79FG)](https:\u002F\u002Fcodecov.io\u002Fgithub\u002FNVIDIA\u002Fwarp)\n![GitHub - CI](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Factions\u002Fworkflows\u002Fci.yml\u002Fbadge.svg)\n\n# NVIDIA Warp\n\n**[Documentation](https:\u002F\u002Fnvidia.github.io\u002Fwarp\u002F)** | [Changelog](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002FCHANGELOG.md)\n\nWarp is a Python framework for GPU-accelerated simulation, robotics, and machine learning. Warp takes\nregular Python functions and JIT compiles them to efficient kernel code that can run on the CPU or GPU.\n\nWarp comes with a rich set of primitives for physics simulation, robotics, geometry processing,\nand more. Warp kernels are differentiable and can be used as part of machine-learning pipelines\nwith frameworks such as PyTorch, JAX and Paddle.\n\n\u003Cdiv align=\"center\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fraw\u002Fmain\u002Fdocs\u002Fimg\u002Fheader.jpg\">\n    \u003Cp>\u003Ci>A selection of physical simulations computed with Warp\u003C\u002Fi>\u003C\u002Fp>\n\u003C\u002Fdiv>\n\n## Quick Start\n\nSimulate one million particles under gravitational attraction, in 20 lines:\n\n```python\nimport warp as wp\nimport numpy as np\n\nnum_particles = 1_000_000\ndt = 0.01\n\n@wp.kernel\ndef gravity_step(pos: wp.array[wp.vec3], vel: wp.array[wp.vec3]):\n    i = wp.tid()\n    position = pos[i]\n    dist_sq = wp.length_sq(position) + 0.01  # softened distance\n    acc = -1000.0 \u002F dist_sq * wp.normalize(position)  # gravitational pull toward origin\n    vel[i] = vel[i] + acc * dt\n    pos[i] = pos[i] + vel[i] * dt\n\nrng = np.random.default_rng(42)\npositions = wp.array(rng.normal(size=(num_particles, 3)), dtype=wp.vec3)\nvelocities = wp.array(rng.normal(size=(num_particles, 3)), dtype=wp.vec3)\n\nfor _ in range(100):\n    wp.launch(gravity_step, dim=num_particles, inputs=[positions, velocities])\n\nprint(positions.numpy())\n```\n\n## Installing\n\nPython version 3.10 or newer is required. Warp can run on x86-64 and ARMv8 CPUs on Windows and Linux, and on Apple Silicon (ARMv8) on macOS.\nGPU support requires a CUDA-capable NVIDIA GPU and driver (minimum GeForce GTX 9xx).\n\nThe easiest way to install Warp is from [PyPI](https:\u002F\u002Fpypi.org\u002Fproject\u002Fwarp-lang\u002F):\n\n```text\npip install warp-lang\n```\n\nYou can also use `pip install warp-lang[examples]` to install additional dependencies for running examples and USD-related features.\n\nFor nightly builds, conda, CUDA 13 builds, building from source, and CUDA driver requirements, see the\n[Installation Guide](https:\u002F\u002Fnvidia.github.io\u002Fwarp\u002Fuser_guide\u002Finstallation.html).\n\n## Tutorial Notebooks\n\nThe [NVIDIA Accelerated Computing Hub](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Faccelerated-computing-hub) contains the current,\nactively maintained set of Warp tutorials:\n\n| Notebook | Colab Link |\n|----------|------------|\n| [Introduction to NVIDIA Warp](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Faccelerated-computing-hub\u002Fblob\u002F32fe3d5a448446fd52c14a6726e1b867cbfed2d9\u002FAccelerated_Python_User_Guide\u002Fnotebooks\u002FChapter_12_Intro_to_NVIDIA_Warp.ipynb) | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FNVIDIA\u002Faccelerated-computing-hub\u002Fblob\u002F32fe3d5a448446fd52c14a6726e1b867cbfed2d9\u002FAccelerated_Python_User_Guide\u002Fnotebooks\u002FChapter_12_Intro_to_NVIDIA_Warp.ipynb) |\n| [GPU-Accelerated Ising Model Simulation in NVIDIA Warp](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Faccelerated-computing-hub\u002Fblob\u002F32fe3d5a448446fd52c14a6726e1b867cbfed2d9\u002FAccelerated_Python_User_Guide\u002Fnotebooks\u002FChapter_12.1_IsingModel_In_Warp.ipynb) | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FNVIDIA\u002Faccelerated-computing-hub\u002Fblob\u002F32fe3d5a448446fd52c14a6726e1b867cbfed2d9\u002FAccelerated_Python_User_Guide\u002Fnotebooks\u002FChapter_12.1_IsingModel_In_Warp.ipynb) |\n\nAdditionally, several notebooks in the [notebooks](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Ftree\u002Fmain\u002Fnotebooks) directory\nprovide additional examples and cover key Warp features:\n\n| Notebook | Colab Link |\n|----------|------------|\n| [Warp Core Tutorial: Basics](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fcore_01_basics.ipynb) | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fcore_01_basics.ipynb) |\n| [Warp Core Tutorial: Generics](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fcore_02_generics.ipynb) | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fcore_02_generics.ipynb) |\n| [Warp Core Tutorial: Points](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fcore_03_points.ipynb) | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fcore_03_points.ipynb) |\n| [Warp Core Tutorial: Meshes](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fcore_04_meshes.ipynb) | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fcore_04_meshes.ipynb) |\n| [Warp Core Tutorial: Volumes](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fcore_05_volumes.ipynb) | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fcore_05_volumes.ipynb) |\n| [Warp PyTorch Tutorial: Basics](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fpytorch_01_basics.ipynb) | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fpytorch_01_basics.ipynb) |\n| [Warp PyTorch Tutorial: Custom Operators](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fpytorch_02_custom_operators.ipynb) | [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fnotebooks\u002Fpytorch_02_custom_operators.ipynb) |\n\n## Running Examples\n\nThe [warp\u002Fexamples](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Ftree\u002Fmain\u002Fwarp\u002Fexamples) directory contains examples\ncovering physics simulation, geometry processing, optimization, and tile-based GPU programming.\nBefore running examples, install the optional example dependencies using:\n\n```text\npip install warp-lang[examples]\n```\n\nOn Linux aarch64 systems (e.g., NVIDIA DGX Spark), the `[examples]` extra automatically installs\n[`usd-exchange`](https:\u002F\u002Fpypi.org\u002Fproject\u002Fusd-exchange\u002F) instead of `usd-core` as a drop-in replacement,\nsince `usd-core` wheels are not available for that platform.\n\nExamples can be run from the command-line as follows:\n\n```text\npython -m warp.examples.\u003Cexample_subdir>.\u003Cexample>\n```\n\nMost examples can be run on either the CPU or a CUDA-capable device, but a handful require a CUDA-capable device. These are marked at the top of the example script. Some examples generate USD files containing time-sampled animations in the current working directory. These can be viewed in Pixar's UsdView, Blender, or any USD-compatible viewer.\n\nTo browse the example source code, you can open the directory where the files are located like this:\n\n```text\npython -m warp.examples.browse\n```\n\n### warp\u002Fexamples\u002Fcore\n\n\u003Ctable>\n    \u003Ctbody>\n        \u003Ctr>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_dem.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_dem.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_fluid.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_fluid.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_graph_capture.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_graph_capture.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_marching_cubes.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_marching_cubes.png\">\u003C\u002Fa>\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd align=\"center\">dem\u003C\u002Ftd>\n            \u003Ctd align=\"center\">fluid\u003C\u002Ftd>\n            \u003Ctd align=\"center\">graph capture\u003C\u002Ftd>\n            \u003Ctd align=\"center\">marching cubes\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_mesh.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_mesh.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_nvdb.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_nvdb.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_raycast.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_raycast.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_raymarch.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_raymarch.png\">\u003C\u002Fa>\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd align=\"center\">mesh\u003C\u002Ftd>\n            \u003Ctd align=\"center\">nvdb\u003C\u002Ftd>\n            \u003Ctd align=\"center\">raycast\u003C\u002Ftd>\n            \u003Ctd align=\"center\">raymarch\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_sample_mesh.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_sample_mesh.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_sph.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_sph.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_torch.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_torch.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_wave.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_wave.png\">\u003C\u002Fa>\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd align=\"center\">sample mesh\u003C\u002Ftd>\n            \u003Ctd align=\"center\">sph\u003C\u002Ftd>\n            \u003Ctd align=\"center\">torch\u003C\u002Ftd>\n            \u003Ctd align=\"center\">wave\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Fcore\u002Fexample_fft_poisson_navier_stokes_2d.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Fcore_fft_poisson_navier_stokes_2d.png\">\u003C\u002Fa>\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd align=\"center\">2-D incompressible turbulence in a periodic box\u003C\u002Ftd>\n        \u003C\u002Ftr>\n    \u003C\u002Ftbody>\n\u003C\u002Ftable>\n\n### warp\u002Fexamples\u002Ffem\n\n\u003Ctable>\n    \u003Ctbody>\n        \u003Ctr>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_diffusion_3d.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_diffusion_3d.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_mixed_elasticity.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_mixed_elasticity.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_apic_fluid.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_apic_fluid.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_streamlines.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_streamlines.png\">\u003C\u002Fa>\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd align=\"center\">diffusion 3d\u003C\u002Ftd>\n            \u003Ctd align=\"center\">mixed elasticity\u003C\u002Ftd>\n            \u003Ctd align=\"center\">apic fluid\u003C\u002Ftd>\n            \u003Ctd align=\"center\">streamlines\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_distortion_energy.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_distortion_energy.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_taylor_green.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_taylor_green.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_kelvin_helmholtz.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_kelvin_helmholtz.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_magnetostatics.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_magnetostatics.png\">\u003C\u002Fa>\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd align=\"center\">distortion energy\u003C\u002Ftd>\n            \u003Ctd align=\"center\">taylor green\u003C\u002Ftd>\n            \u003Ctd align=\"center\">kelvin helmholtz\u003C\u002Ftd>\n            \u003Ctd align=\"center\">magnetostatics\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_adaptive_grid.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_adaptive_grid.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_nonconforming_contact.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_nonconforming_contact.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_darcy_ls_optimization.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_darcy_ls_optimization.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ffem\u002Fexample_elastic_shape_optimization.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ffem_elastic_shape_optimization.png\">\u003C\u002Fa>\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd align=\"center\">adaptive grid\u003C\u002Ftd>\n            \u003Ctd align=\"center\">nonconforming contact\u003C\u002Ftd>\n            \u003Ctd align=\"center\">darcy level-set optimization\u003C\u002Ftd>\n            \u003Ctd align=\"center\">elastic shape optimization\u003C\u002Ftd>\n        \u003C\u002Ftr>\n    \u003C\u002Ftbody>\n\u003C\u002Ftable>\n\n### warp\u002Fexamples\u002Foptim\n\n\u003Ctable>\n    \u003Ctbody>\n        \u003Ctr>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Foptim\u002Fexample_diffray.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Foptim_diffray.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Foptim\u002Fexample_fluid_checkpoint.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Foptim_fluid_checkpoint.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Foptim\u002Fexample_particle_repulsion.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Foptim_particle_repulsion.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Foptim\u002Fexample_navier_stokes_perturbation.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Foptim_navier_stokes_perturbation.png\">\u003C\u002Fa>\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd align=\"center\">diffray\u003C\u002Ftd>\n            \u003Ctd align=\"center\">fluid checkpoint\u003C\u002Ftd>\n            \u003Ctd align=\"center\">particle repulsion\u003C\u002Ftd>\n            \u003Ctd align=\"center\">navier-stokes perturbation\u003C\u002Ftd>\n        \u003C\u002Ftr>\n    \u003C\u002Ftbody>\n\u003C\u002Ftable>\n\n### warp\u002Fexamples\u002Ftile\n\n\u003Ctable>\n    \u003Ctbody>\n        \u003Ctr>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ftile\u002Fexample_tile_mlp.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ftile_mlp.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ftile\u002Fexample_tile_nbody.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ftile_nbody.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Fwarp\u002Fexamples\u002Ftile\u002Fexample_tile_mcgp.py\">\u003Cimg src=\"https:\u002F\u002Fmedia.githubusercontent.com\u002Fmedia\u002FNVIDIA\u002Fwarp\u002Frefs\u002Fheads\u002Fmain\u002Fdocs\u002Fimg\u002Fexamples\u002Ftile_mcgp.png\">\u003C\u002Fa>\u003C\u002Ftd>\n            \u003Ctd width=\"25%\">\u003C\u002Ftd>\n        \u003C\u002Ftr>\n        \u003Ctr>\n            \u003Ctd align=\"center\">mlp\u003C\u002Ftd>\n            \u003Ctd align=\"center\">nbody\u003C\u002Ftd>\n            \u003Ctd align=\"center\">mcgp\u003C\u002Ftd>\n            \u003Ctd align=\"center\">\u003C\u002Ftd>\n        \u003C\u002Ftr>\n    \u003C\u002Ftbody>\n\u003C\u002Ftable>\n\n## Learn More\n\nPlease see the following resources for additional background on Warp:\n\n* [Product Page](https:\u002F\u002Fdeveloper.nvidia.com\u002Fwarp-python)\n* [SIGGRAPH 2024 Course Slides](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3664475.3664543)\n* [GTC 2024 Presentation](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fon-demand\u002Fsession\u002Fgtc24-s63345\u002F)\n* [GTC 2022 Presentation](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fon-demand\u002Fsession\u002Fgtcspring22-s41599)\n* [GTC 2021 Presentation](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fon-demand\u002Fsession\u002Fgtcspring21-s31838)\n* [SIGGRAPH Asia 2021 Differentiable Simulation Course](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3476117.3483433)\n\n## Support\n\nSee the [FAQ](https:\u002F\u002Fnvidia.github.io\u002Fwarp\u002Fuser_guide\u002Ffaq.html) for common questions.\n\nProblems, questions, and feature requests can be opened on [GitHub Issues](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fissues).\n\nFor inquiries not suited for GitHub Issues, please email \u003Cwarp-python@nvidia.com>.\n\n## Contributing\n\nContributions and pull requests from the community are welcome.\nPlease see the [Contribution Guide](https:\u002F\u002Fnvidia.github.io\u002Fwarp\u002Fuser_guide\u002Fcontribution_guide.html) for more\ninformation on contributing to the development of Warp.\n\n## License\n\nWarp is provided under the Apache License, Version 2.0.\nPlease see [LICENSE.md](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002FLICENSE.md) for full license text.\n\nThis project will download and install additional third-party open source software projects.\nReview the license terms of these open source projects before use.\n\n### Building from Source\n\nWhen building Warp from source using the `build_lib.py` script, the build process automatically\ndownloads [NVIDIA libmathdx](https:\u002F\u002Fdeveloper.nvidia.com\u002Fcublasdx-downloads). Pre-built Warp\npackages (e.g., from PyPI) already include libmathdx statically linked into the library binaries.\nIn both cases, libmathdx is governed by the\n[NVIDIA Software License Agreement](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002Flicenses\u002Flibmathdx-LICENSE.txt).\n\nNOTICE AND DISCLAIMER: This software automatically retrieves, accesses or interacts with external\nmaterials. Those retrieved materials are not distributed with this software and are governed solely\nby separate terms, conditions and licenses. You are solely responsible for finding, reviewing and\ncomplying with all applicable terms, conditions, and licenses, and for verifying the security,\nintegrity and suitability of any retrieved materials for your specific use case. This software is\nprovided \"AS IS\", without warranty of any kind. The author makes no representations or warranties\nregarding any retrieved materials, and assumes no liability for any losses, damages, liabilities or\nlegal consequences from your use or inability to use this software or any retrieved materials. Use\nthis software and the retrieved materials at your own risk.\n\n## Publications & Citation\n\n### Research Using Warp\n\nOur [PUBLICATIONS.md](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002FPUBLICATIONS.md) file lists academic and research\npublications that leverage the capabilities of Warp.\nWe encourage you to add your own published work using Warp to this list.\n\n### Citing Warp\n\nIf you use Warp in your research, please use the \"Cite this repository\" button on the\n[GitHub repository](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp) page or refer to the\n[CITATION.cff](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp\u002Fblob\u002Fmain\u002FCITATION.cff) file for citation information.\n","NVIDIA Warp 是一个用于GPU加速的仿真、机器人技术和机器学习的Python框架。它能够将普通的Python函数即时编译为可在CPU或GPU上运行的高效内核代码，支持物理模拟、几何处理等多种原语，并且这些内核是可微分的，可以与PyTorch、JAX等机器学习框架结合使用。特别适合需要高性能计算支持的研究者和开发者在进行复杂物理现象模拟、机器人控制算法开发以及深度学习模型训练时采用。",2,"2026-06-11 03:35:51","high_star"]