Notice: Support for Python 3.6 will be dropped in v.0.2.1, please plan accordingly!

Efficient and Scalable Physics-Informed Deep Learning

Collocation-based PINN PDE solvers for prediction and discovery methods on top of Tensorflow 2.X for multi-worker distributed computing.

Use TensorDiffEq if you require:

• A meshless PINN solver that can distribute over multiple workers (GPUs) for forward problems (inference) and inverse problems (discovery)
• Scalable domains - Iterated solver construction allows for N-D spatio-temporal support
  • support for N-D spatial domains with no time element is included
• Self-Adaptive Collocation methods for forward and inverse PINNs
• Intuitive user interface allowing for explicit definitions of variable domains, boundary conditions, initial conditions, and strong-form PDEs

What makes TensorDiffEq different?

• Completely open-source

• Self-Adaptive Solvers for forward and inverse problems, leading to increased accuracy of the solution and stability in training, resulting in less overall training time

• Multi-GPU distributed training for large or fine-grain spatio-temporal domains

• Built on top of Tensorflow 2.0 for increased support in new functionality exclusive to recent TF releases, such as XLA support, autograph for efficent graph-building, and grappler support for graph optimization* - with no chance of the source code being sunset in a further Tensorflow version release

• Intuitive interface - defining domains, BCs, ICs, and strong-form PDEs in "plain english"

*In development

If you use TensorDiffEq in your work, please cite it via:

@article{mcclenny2021tensordiffeq,
  title={TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks},
  author={McClenny, Levi D and Haile, Mulugeta A and Braga-Neto, Ulisses M},
  journal={arXiv preprint arXiv:2103.16034},
  year={2021}
}

Thanks to our additional contributors:

@marcelodallaqua, @ragusa, @emiliocoutinho