发表机构
Google; Department of Computer Science, ETH Zürich（谷歌; 苏黎世联邦理工学院计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文展示了一个基于JAX的、不足380行代码的黑洞光线追踪器，证明AI加速器能简化GPU数值应用开发，并使广义相对论对计算机科学专业人士更易理解，同时激励以可微算法表达物理定律的新方法。
AI 中文摘要
我们展示了主要为AI应用构建的软件基础设施也能极大地惠及其他用途——通过一个完全自包含的、基于JAX的黑洞光线追踪器，其代码量不足180+200行Python代码（含文档）。展示这种构建的主要动机有：(a) 向专业物理学家展示机器学习（ML）加速器如何能以极少的编码工作和编程专业知识，极大地简化用于GPU加速硬件的数值应用的构建——即使所处理的问题本身并不涉及ML；(b) 使广义相对论（GR）中有意义的部分对计算机科学专业人士变得可及，他们可能没有意识到，他们在深度学习问题上培养的数学背景可能已使该理论触手可及；(c) 激励学生和教育工作者探索一种方法的潜力，该方法将物理定律（即局部微分量之间的关系）不以可微符号表达式来表示，而是以可微算法来表示。
英文摘要
We show how software infrastructure that was mainly built to power AI applications also can be used to great benefit for other purposes -- by demonstrating a fully self-contained JAX-based black hole raytracer in under 180+200 lines of Python code plus documentation. Major motivations for showcasing such a construction are (a) to show to professional physicists how Machine Learning (ML) accelerators can greatly simplify building numerical applications for GPU accelerator hardware with very modest coding effort and programming expertise -- even if the problems at hand do not involve ML per se, (b) to make a meaningful part of General Relativity (GR) accessible to computer science professionals who might not have realized that mathematical background they developed on Deep Learning problems may have brought this theory within easy reach for them, and (c) to inspire students and educators to explore the potential of an approach that represents physical law, i.e. relations between local differential quantities, not in terms of differentiable symbolic expressions, but in terms of differentiable algorithms.
Comments28+20 pages, 9 figures. Git repository at https://github.com/paradigms-of-intelligence/raytracing-black-holes-with-jax
