Pressure Map

Pressure maps represent the distribution of pressure across a surface, with applications ranging from aerodynamic analysis to healthcare. Current research focuses on developing accurate and efficient methods for predicting and reconstructing pressure maps using diverse data sources (e.g., images, sensor readings) and advanced machine learning techniques, including convolutional autoencoders, variational autoencoders, and neural operators. These advancements improve the accuracy and efficiency of pressure map generation, leading to better understanding of complex systems and enabling new applications in diverse fields such as sports analytics, medical diagnostics, and engineering design. The development of robust and generalizable models is a key focus, aiming to improve prediction accuracy and reduce computational costs.