HEAP, a hierarchical autoencoder with a predictor that advances multiple scale layers in sync, achieves several-fold lower long-term rollout error than flat ResNet baselines on Hasegawa-Wakatani turbulence.
Climformer-a spherical transformer model for long-term climate projections
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Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems
HEAP, a hierarchical autoencoder with a predictor that advances multiple scale layers in sync, achieves several-fold lower long-term rollout error than flat ResNet baselines on Hasegawa-Wakatani turbulence.