Show Your Work

We’d love to see what you’ve built with SymTorch!

Why Use SymTorch?

SymTorch bridges the gap between the power of deep learning and the interpretability of symbolic mathematics. By combining neural networks with symbolic regression, you can:

  • Discover interpretable equations from your trained models

  • Understand what your neural networks have learned at both layer and model levels

  • Extract symbolic knowledge from black-box models

  • Create explainable AI systems that are both accurate and interpretable

  • Accelerate scientific discovery by uncovering mathematical relationships in your data

Whether you’re working on physics-informed neural networks, graph neural networks, or classical machine learning problems, SymTorch provides the tools to transform learned representations into human-readable mathematical expressions.

Share Your Work With Us

Have you used SymTorch in your research, project, or application? We want to hear from you!

We’re looking for:

  • Research papers using SymTorch

  • Industry applications and case studies

  • Educational materials and tutorials

  • Novel use cases and creative applications

  • Performance benchmarks and comparisons

  • Integration with other tools and frameworks

What we’ll showcase:

  • Links to your papers, repositories, or blog posts

  • Brief descriptions of your application

  • Key results and insights

  • Visualizations or demonstrations (if available)

  • Your contact information (if you’d like to share it)

Benefits of Sharing

By sharing your work, you’ll:

  • Gain visibility for your research or project within the community

  • Inspire others to explore new applications of symbolic regression

  • Contribute to the growing ecosystem of interpretable machine learning

  • Connect with other researchers and practitioners in the field

  • Help shape the future development of SymTorch based on real-world use cases

Example Use Cases

SymTorch is versatile and can be applied to various domains:

Scientific Computing

  • Discovering physical laws from experimental data

  • Simplifying complex physics-informed neural networks (PINNs)

  • Extracting governing equations from dynamical systems

Machine Learning

  • Creating interpretable alternatives to black-box models

  • Understanding intermediate representations in deep networks

  • Model compression through symbolic approximation

Domain-Specific Applications

  • Climate modeling and weather prediction

  • Financial modeling and risk analysis

  • Biological systems and computational biology

  • Materials science and chemistry

We Want to Hear Your Story

Every application of SymTorch is unique, and we’re excited to learn how you’re using symbolic regression to make neural networks more interpretable.

Whether you’re a researcher publishing a paper, a student working on a class project, or an industry practitioner solving real-world problems, your work matters to the community.

Don’t hesitate to reach out - we’d love to feature your work here!

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Last updated: 2025