This Python Package Makes Differentiable Physics Simulations Practical
Last Updated on January 2, 2026 by Editorial Team
Author(s): Gowtham Boyina
Originally published on Towards AI.
It’s from NVIDIA, it's not CUDA
I’ve spent way too long fighting with CUDA just to prototype a simple physics simulation. You either hand-roll low-level kernels in C++ — which breaks your Python workflow — or you accept glacial performance from NumPy or pure PyTorch. Neither option feels right if you’re iterating fast in research or building spatial computing pipelines.
Warp is a Python framework that enables high-performance, differentiable physics simulations without the typical hassle of CUDA programming. It provides a domain-specific language with a Just-In-Time (JIT) compiler that translates Python functions into optimized CPU or GPU kernels, allowing for seamless integration with existing machine learning frameworks. Warp is particularly effective for applications in robotics, geometry processing, and spatial computing, offering built-in geometric primitives and advanced differentiability features that enhance simulation capabilities and ease the prototyping process for researchers and developers working in these domains.
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