Solving partial differential equations (PDEs) is complex, just like the events they explain. These equations help determine how things change over space and time, and they’re used to model everything from tiny quantum interactions to huge space phenomena. Earlier methods of solving these equations struggled with the challenge of changes happening over time. Getting accurate answers depends on understanding these changes well. However, it’s tough to do this, especially when changes occur at different scales or levels. Deep learning, using designs like U-Nets, is popular for working with information at multiple levels of detail. However, there’s a big problem: temporal