Resolution Simulation

Resolution simulation aims to enhance the detail of computationally expensive simulations by leveraging machine learning to infer high-resolution outputs from lower-resolution inputs. Current research focuses on applying deep learning architectures, such as U-Nets, convolutional neural networks, and Fourier neural operators, to various physical systems, including fluid dynamics, climate modeling, and astrophysics, often incorporating physics-informed losses to improve accuracy. This approach offers significant potential for accelerating scientific discovery by reducing computational costs and enabling higher-fidelity simulations of complex phenomena, ultimately improving predictions in fields like weather forecasting and climate change modeling.