Deep Wave Network for Modeling Multi-Scale Physical Dynamics

ArXi:2605.04198v1 Announce Type: cross Performance of deep learning models is strongly governed by architectural capacity, with width and depth as primary controls. However, in physical-science applications, models are often compared at a single fixed size or by separating accuracy and computational cost, which can be misleading since architectures exhibit different accuracy-cost scaling as width and depth vary.