Update pillow library to fix CVE, update docs (#210)

Dockerfile

@@ -48,7 +48,7 @@ RUN if [ "$TARGETPLATFORM" = "linux/arm64" ] && [ -e "/modulus-sym/deps/vtk-9.2.
 # Install modulus sym dependencies
 RUN pip install --no-cache-dir "hydra-core>=1.2.0" "termcolor>=2.1.1" "chaospy>=4.3.7" "Cython==0.29.28" "numpy-stl==2.16.3" "opencv-python>=4.8.1.78" \
     "scikit-learn>=1.0.2" "symengine>=0.10.0" "sympy>=1.12" "timm>=1.0.3" "torch-optimizer==0.3.0" "transforms3d==0.3.1" \
-    "typing==3.7.4.3" "pillow==10.2.0" "notebook>=7.2.2" "mistune==2.0.3" "pint==0.19.2" "tensorboard>=2.8.0"
+    "typing==3.7.4.3" "pillow==10.3.0" "notebook>=7.2.2" "mistune==2.0.3" "pint==0.19.2" "tensorboard>=2.8.0"
 
 # Install warp-lang
 RUN pip install --no-cache-dir warp-lang

docs/api/modulus.sym.models.rst

@@ -8,6 +8,13 @@ models.afno
    :members: AFNOArch
    :show-inheritance:
 
+models.deeponet
+-------------------------------
+
+.. automodule:: modulus.sym.models.deeponet
+   :members: DeepONetArch
+   :show-inheritance:
+
 models.dgm
 -------------------------------
 

modulus/sym/eq/phy_informer.py

@@ -89,7 +89,8 @@ class PhysicsInformer:
             The `.forward` call requires input dict with the relevant variables in
             `[N, 1, H, W, D]` for 3D, `[N, 1, H, W]` for 2D and `[N, 1, H]` for 1D.
             `least_squares`: The spatial gradients are computed using Least Squares
-            technique. Ideal for use with mesh based representations. All values are
+            technique. Ideal for use with mesh based representations (i.e. unstructured
+            grids). All values are
             computed at the nodes. The `.forward` call requires input dict with
             the relevant variables in `[N, 1]` shape along with entry for "coordinates"
             in `[N, m]` shape where m is the dimensionality of the input

modulus/sym/eq/spatial_grads/spatial_grads.py

@@ -651,7 +651,7 @@ def forward(self, input_dict):
 class GradientsLeastSquares(torch.nn.Module):
     """
     Compute spatial derivatives using Least Squares technique modified to compute
-    gradients on nodes.
+    gradients on nodes. Useful for mesh based representations (i.e. unstructured grids)
 
     Reference: https://scientific-sims.com/cfdlab/Dimitri_Mavriplis/HOME/assets/papers/aiaa20033986.pdf
 

pyproject.toml

@@ -22,7 +22,7 @@ dependencies = [
     "numpy-stl>=2.16,<2.17",
     "nvidia-modulus>=0.2.0",
     "opencv-python>=4.8.1.78",
-    "pillow>=10.2,<10.3",
+    "pillow>=10.3,<10.4",
     "pint>=0.19.2",
     "scikit-learn>=1.2.0",
     "symengine>=0.10.0",