v1.7.0

Modulus Symbolic general release v1.7.0

Added

• AMP for derivatives.
• DALI based dataloader for Geometry module.
• Generalized PDE residual computing utility.
• Support for spatial gradients calculations using finite difference, meshless finite
  difference, spectral and least squares methods.
• Add docs for SDF and relevant features for geometry module.

Dependencies

• Upgrade Sympy and Scikit-Learn versions.

Security

• Upgrade notebook, opencv-python and setuptools versions to fix CVEs GHSA-9q39-rmj3-p4r2,
  GHSA-qr4w-53vh-m672, and GHSA-cx63-2mw6-8hw5 respectively.

v1.6.0

Modulus Symbolic general release v1.6.0

Added

• Added inference notebook for Turbulence Super-resolution example.

Changed

• Warp based backed for STL geometry handling

Dependencies

• Update timm dependency
• Update minimum python version to 3.10

v1.5.0

Modulus Symbolic general release v1.5.0

Added

• Added reservoir examples using GenAI and CCUS workflows.

Security

• Update OpenCV and Pillow versions to fix security

v1.4.0

Modulus Symbolic general release v1.4.0

Fixed

• Fix bug for ConvFullyConnectedArch.
• Updating Activation.SILU test to conform with updated nvFuser kernel generation.

v1.3.0

Modulus Symbolic general release v1.3.0

Added

• Added instructions for docker build on ARM architecture.
• Added domain decomposition examples using X-PINN and FB-PINN style.

Changed

• Integrated the network architecture layers into Modulus-Core.

Fixed

• Fixed Gradient Aggregation bug.

Security

• Upgrade Pillow and Sympy to their latest versions.

Dependencies

• Updated base PyTorch container to 23.10 and Optix version to 7.3.0

v1.2.0

Modulus Symbolic general release v1.2.0

Added

• Example for reservoir modeling using PINO and FNO

v1.1.0

Modulus Symbolic general release v1.1.0

Added

• Added a CHANGELOG.md

Removed

• Accompanying licenses (will provide in the Modulus docker image).

Fixed

• Arch from_config bug for literal params.
• Fixed fused SiLU activation test.
• Update np.bool to np.bool_.
• Added a workaround fix for the CUDA graphs error in multi-node runs

Dependencies

• Updated the base container to latest PyTorch base container which is based on torch 2.0
• Container now supports CUDA 12, Python 3.10
• Updated symengine to >=0.10.0 and vtk to >=9.2.6

v1.0.0

Modulus symbolic general release v1.0.0

(Initial OSS release)