PINNStudio: A free, open-source no-code GUI for setting up, training, and visualizing PINNs [P]
PINNStudio 是一个免费、开源、无代码的图形用户界面，旨在简化物理信息神经网络（PINNs）的设置、训练和可视化。它利用 DeepXDE 自动生成代码，运行模型，实时传输训练日志，并在应用程序内显示实时损失曲线和解决方案图。该工具内置了经典方程模板，如热方程、Allen-Cahn 和 Cahn-Hilliard，旨在减少手动修改偏微分方程、边界条件和架构所需的工作量。
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- 2026年9月6日 22:19
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When I first started working in scientific machine learning, I understood the physics much better than the coding. Every time I wanted to try a new physics-informed neural network problem, I had to start almost from scratch: changing the PDE, updating boundary conditions, modifying the architecture, tweaking the training schedule, debugging errors, and generating plots—all by hand.
That frustration pushed me to build PINNStudio. It is a free, open-source no-code GUI designed to eliminate boilerplate code so you can focus entirely on the physics.
Instead of rewriting a new script for every problem, you can define your setup directly through the interface:
- PDE Definitions & coupled multi-output PDE systems
- 1D or 2D domains with boundary and initial conditions
- Network architecture & custom training schedules
- Forward problems (solving known PDEs) or Inverse problems (estimating unknown parameters from data)
What happens next? PINNStudio automatically generates the code (built on top of DeepXDE), runs the model, streams the training log, and displays live loss curves and solution plots directly inside the app. It also includes built-in templates for classic equations like Heat, Allen-Cahn, and Cahn-Hilliard.
- GitHub (Open Source): https://github.com/AsfandyarKhan72/PINNStudio
- Quick Install: pip install pinnstudio
My hope is that this will be helpful for students and researchers with limited coding experience, as well as experienced PINN users who just want a faster workflow.
I’d love to get your feedback, feature suggestions, or bug reports! Huge thanks to Lu Lu and the DeepXDE team for creating the foundation that made this possible.