Rckirby/getavec

.flake8

@@ -0,0 +1,5 @@
+[flake8]
+ignore = E203, E266, E501, W503, F403, F401, F405
+max-line-length = 79
+max-complexity = 18
+select = B,C,E,F,W,T4,B9

.gitignore

@@ -3,3 +3,8 @@ tests/__pycache__/test.cpython-310-pytest-6.2.5.pyc
 tests/__pycache__/test_fd_to_torch.cpython-310-pytest-6.2.5.pyc
 src/torchfire/__pycache__/__init__.cpython-310.pyc
 src/torchfire/__pycache__/torchfire.cpython-310.pyc
+
+demos/VisualizingResults/
+demos/*/torchfire.py
+ml_templates/*
+Convert_data_to_Firedrake/*

README.md

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-# torchfire
-Firedrake provides a high-level library for the automated solution of PDE by finite element methods.  
-In order to use Firedrake as a basis for scientific machine learning, we need to make it callable within a learning framework sy as PyTorch.
-This package provides such bindings by means of extending PyTorch.Function, the base class for user-defined operations.  
+# Torchfire
+Firedrake provides a high-level library for the automated solution of PDE by finite element methods. In order to use Firedrake as a basis for scientific machine learning, we need to make it callable within a learning framework sy as PyTorch. This package provides such bindings by means of extending PyTorch.Function, the base class for user-defined operations.  
+
+# Numerical problems
+## Problem 1: Solving heat equation with nFEM-TorchFire
+
+<p align="center">
+<img width="800" height = "250" src="demos/nfem_heat_equation/results/animations.gif">
+<figcaption><b>Figure 1:</b> (Left) Ground truth conductivity diffusion, (Middle) Temperature field obtained by Firedrake software, (Right) Temperature field obtained by nFEM, TorchFire</figcaption>
+</p>
+
+
+## Problem 2: Conductivity diffusion field inversion from sparse observations with TNet-TorchFire
+<p align="center">
+<img width="550" height = "250" src="demos/tnet_heat_equation/results/animations.gif">
+<figcaption><b>Figure 2:</b> (Left) Ground truth conductivity diffusion, (Right) Predicted Conductivity diffusion obtained by TNet-TorchFire, reconstructed from 10 random observables over the domain</figcaption>
+</p>
+
+

demos/__init__.py

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+from .torchfire import fd_to_torch

demos/nfem_heat_equation/data/Degree_of_Freedom_indices.csv

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