Month 2025-09: Focused on maintainability improvements in the Autograd module of tidy3d. Implemented a refactor that splits the autograd functionality into smaller, cohesive sub-modules while preserving existing behavior, enabling easier future feature work and safer evolutions of the codebase.

June 2025 performance summary focused on accelerating differentiable design workflows and practical adjoint-method exploration across tidy3d and tidy3d-notebooks. Delivered autograd-enabled differentiation for S-matrix calculations, enabling gradient-based optimization for scattering-related objectives and aligning data handling and simulation execution with autograd. Integrated adjoint simulations into the Metalens notebook, including refactoring to support adjoint solvers and visualization for end-to-end exploration. These efforts establish a robust, differentiable pipeline that accelerates design iterations, improves optimization results, and enhances researcher productivity across core models and notebooks.

Monthly work summary for 2024-12 focusing on reliability, data interchange, and visualization for tidy3d. Delivered feature improvements in visualization, standardized geometry handling, and fixed critical JSON edge-case handling, with tests and changelog updates.

Focused on delivering gradient-based optimization capabilities and robust documentation/workflow improvements. Key features delivered include differentiable geometry support for PolySlab and Cylinder (Autograd), enhancements to autograd accuracy and gradient monitoring for PolySlab, and new background medium support. Major bug fixes address gradient normalization across multi-frequency monitors and polyslab edge gradient assignments. The month concluded with a project-wide 2.7.7 release, synchronized docs/notebooks submodules, and expanded autograd tests and FAID notebook support.

In October 2024, delivered key features across tidy3d and related notebooks, focusing on consistency, reliability, and showcasing auto-differentiation capabilities. Achievements include unifying default simulation_type to tidy3d across simulation components, improving task failure messaging for easier debugging, fixing autograd gradient for Cylinder.center, and introducing an inverse-design demonstration notebook for quantum emitter light extractor using tidy3d autograd. These efforts reduce user error, streamline workflows, and demonstrate end-to-end optimization workflows via Jupyter notebooks.