This code is the Tensorflow implementation of
N. Trask, R. G. Patel, B. J. Gross, and P. J. Atzberger, "GMLS-Nets: A Framework for Learning from Unstructured Data," arXiv:1909.05371, (2019).
pip install gmlsnets-tensorflow
Python >= 3.5
numpy
scipy
matplotlib
scikit-learn
toolz
tensorflow
The three classes in gmlsnets_tensorflow/__init__.py provide Keras layers used to construct GMLS-Nets architectures. MFLayer creates layers that compute GMLS coefficients from functions sampled on a point cloud. MFConvLayer and MFPoolLayer create for point cloud data the equivalent to (strided) convolutional layers and pool layers, respectively. These classes use the weighting functions in gmlsnets_tensorflow/weightfuncs.py and the polynomial bases in gmlsnets_tensorflow/bases.py. See the examples folder for MNIST and PDE discovery examples.
For the PyTorch implementation, see https://github.com/atzberg/gmls-nets.