The downwelling irradiance is a crucial part of the air-sea heat flux and a major energy source for the photosynthesis processes in the upper ocean. The modeling of the irradiance field is often time-consuming in conventional Monte Carlo (MC) simulations. We propose an accelerated model based on machine learning to significantly reduce the computational cost. By introducing a generalized beam spreading function, we transform the raw data generated with the MC simulation into training data of reduced dimensions, which is then used to develop an artificial neural network. To validate the machine learning model, we use the MC model to simulate the irradiance field under a broadband wave field. For both clear and turbid seawater, the irradiance field predicted by the machine learning model agrees well with the MC simulation result. Compared with the MC simulation that is computationally expensive, our machine learning model is O(1,000) times faster.
Bibliographical noteFunding Information:
The authors gratefully acknowledge the referees for their constructive and valuable comments. This work was supported by the Office of Naval Research.
© 2022. The Authors.
- machine learning
- Monte Carlo simulation
- ocean gravity waves
- ocean optics