How Much Memory Do We Need? Adaptive Memory Gate for Neural Operators

This paper addresses the limitation of fixed memory weights in memory-augmented neural operators for solving time-dependent PDEs. The authors propose an adaptive memory gate mechanism that dynamically adjusts memory weights based on observation conditions such as resolution or physical parameters. This approach enhances the adaptability of neural operators without sacrificing the performance achieved by incorporating historical states.