Machine Learning Surrogate

Machine learning surrogates are computationally efficient approximations of complex, time-consuming models used across diverse scientific domains, from hydrology and weather forecasting to exoplanet atmospheric analysis and chemical engineering. Current research focuses on improving surrogate accuracy and efficiency using various machine learning architectures, including deep neural networks, random forests, and Fourier neural operators, often incorporating techniques like data augmentation, transfer learning, and custom loss functions to enhance performance. These surrogates significantly accelerate computationally intensive tasks such as uncertainty quantification, optimization, and data assimilation, enabling more efficient scientific discovery and improved decision-making in various applications.