The Adam method is a refinement algorithm, widely used in deep learning. Unlike synthesis methods, which build up new layer structures, Adam focuses on adjusting the thicknesses of existing layers to improve optical performance.
Adam (Adaptive Moment Estimation) combines the benefits of momentum-based optimization with adaptive learning rates for each parameter. This makes it especially well suited for thin-film design, where multiple layers interact in complex ways and the optimization landscape may be highly non-linear.
The algorithm iteratively updates layer thicknesses by taking into account both the current gradient (how the merit function changes with thickness) and the history of past updates, allowing it to converge faster and more reliably than simple gradient descent.
This iterative process continues until no further improvements can be achieved, or until predefined limits such as maximum epochs are reached.
Advantages
- Efficient Refinement: Improves existing designs effectively.
- Adaptive Learning Rates: Stabilize convergence and accelerate progress.
- Scale Robustness: Less sensitive to poor initial parameter scaling than plain gradient descent.
- Seamless Integration: Works naturally with synthesis methods (e.g., Needle, AnyNeedle) in nested workflows.
Limitations
- Hyperparameter Sensitivity: Depends on well-chosen learning rate, patience, and max epochs.
- Local Minima Risk: Can converge to suboptimal solutions in complex merit landscapes.
- Speed–Stability Trade-off: Requires careful balance between fast updates and stable training.
In FilmOptima
In FilmOptima, Adam belongs to the Refinement category of algorithms. It is the primary refinement algorithm and a cornerstone of FilmOptima’s nested optimization strategy.
| Parameter | Description |
|---|---|
| LearningRate | Controls the step size in the Adam optimizer during refinement. Higher values make updates faster but risk overshooting, while lower values are more stable but slower to converge. |
| Patience | Defines how many iterations the Adam optimizer will continue without improvement in the merit function before halving the learning rate. |
| MaxEpoch | Sets the maximum number of training cycles for the Adam optimizer in each refinement step. Acts as a hard limit to keep optimization runs bounded. |
