Basic Emulator

Basic emulators are computationally efficient machine learning models designed to mimic the outputs of complex, resource-intensive simulations, such as those used in climate modeling, ice sheet dynamics, and particle physics. Current research emphasizes the use of deep generative models, including diffusion models, variational autoencoders, and graph neural networks, to achieve high accuracy and scalability. This work significantly reduces computational costs associated with high-resolution simulations, enabling faster analysis, improved uncertainty quantification, and broader application of complex models across various scientific disciplines and practical applications like flood risk assessment and climate change impact studies.