Torchdyn

Deep learning for complex dynamical systems

Torchdyn is maintained by DiffeqML, an open research group for the intersection of deep learning and dynamical systems

Torchdyn is a PyTorch library and community dedicated to numerical deep learning: differential equations, integral transforms and implicit representations

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Torchdyn-related research and implementations

Try this

Neural differential equations made easy

from torchdyn.core import NeuralODE

# your preferred torch.nn.Module here 
f = nn.Sequential(nn.Conv2d(1, 32, 3),
                  nn.Softplus(),
                  nn.Conv2d(32, 1, 3)
          )

nde = NeuralODE(f)

...and you have a trainable model

Feel free to combine similar torchdyn classes with any PyTorch modules to build composite models. We offer additional tools to build custom neural differential equations and implicit models, including a functional API for numerical methods. There is much more in Torchdyn other than NeuralODE and NeuralSDE classes: tutorials, a functional API to a variety of GPU-compatible numerical methods, benchmarks...

Contribute to the library with your benchmark and model variants! No need to reinvent the wheel :)

Torchdyn tutorials, API, Contribution Guide and more

Docs

Institutions using Torchdyn

  • Mila
  • Standford University
  • Syntensor
  • KAIST