Generalizable turbulent flow forecasting for adaptive optics control.

Area of Science:

• Fluid dynamics
• Artificial intelligence
• Optical engineering

Background:

• Predictive adaptive optics (AO) aims to correct wavefront distortions caused by turbulence.
• Current AO systems face challenges in dynamic environments with varying turbulence conditions.
• Generalizable turbulence forecasting is crucial for reliable AO performance.

Purpose of the Study:

• To assess artificial neural network (ANN) models for generalizable turbulence forecasting.
• To evaluate the capability of ANNs in supporting predictive AO applications.
• To enable continuous predictive AO operation in dynamic environments.

Main Methods:

• Developed and trained ANN predictive models on a specific set of turbulent flow conditions.
• Tested model performance on diverse compressible flow conditions not present in the training data.
• Compared ANN model prediction error against a hypothetical baseline assuming perfect prior knowledge.

Main Results:

• The ANN model demonstrated consistent high performance across various flow conditions beyond the training set.
• Prediction error increased only minimally compared to a hypothetical perfect-information model.
• The model effectively extracted relevant dynamics from limited turbulent conditions for forecasting.

Conclusions:

• ANN models are capable of generalizable turbulence forecasting for predictive AO.
• This approach enhances AO system robustness and reduces sensitivity to changing environmental conditions.
• The findings can guide the design of future predictive AO systems.