论文标题
结合高性能硬件，云计算和深度学习框架以加速物理模拟：探索Hopfield网络
Combining high-performance hardware, cloud computing, and deep learning frameworks to accelerate physical simulations: probing the Hopfield network
论文作者
论文摘要
高性能计算（尤其是图形处理单元），云计算服务（例如Google Colab）和高级深度学习框架（例如Pytorch）的合成为人工智能的新兴领域提供了支持。尽管这些技术在计算机科学纪律中很受欢迎，但物理学界不太了解这种创新如何在线上可以改善研究和教育。在本教程中，我们以Hopfield网络为例，以说明这些领域的汇合如何显着加速基于物理的计算机模拟并消除实施此类程序时的技术障碍，从而使物理实验和教育更快，更容易访问。为此，我们介绍了可以轻松重新用于物理模拟的云，GPU和AI框架。然后，我们介绍了Hopfield网络，并解释了如何在云中免费产生大规模的模拟和可视化，几乎没有代码（文本中完全独立）。最后，我们建议整本论文进行编程练习，旨在研究物理学，生物物理学或计算机科学的高级本科生。
The synthesis of high-performance computing (particularly graphics processing units), cloud computing services (like Google Colab), and high-level deep learning frameworks (such as PyTorch) has powered the burgeoning field of artificial intelligence. While these technologies are popular in the computer science discipline, the physics community is less aware of how such innovations, freely available online, can improve research and education. In this tutorial, we take the Hopfield network as an example to show how the confluence of these fields can dramatically accelerate physics-based computer simulations and remove technical barriers in implementing such programs, thereby making physics experimentation and education faster and more accessible. To do so, we introduce the cloud, the GPU, and AI frameworks that can be easily repurposed for physics simulation. We then introduce the Hopfield network and explain how to produce large-scale simulations and visualizations for free in the cloud with very little code (fully self-contained in the text). Finally, we suggest programming exercises throughout the paper, geared towards advanced undergraduate students studying physics, biophysics, or computer science.
