Meta-Learning Dynamics Forecasting Using Task Inference. (arXiv:2102.10271v4 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2102.10271

Current deep learning models for dynamics forecasting struggle with
generalization. They can only forecast in a specific domain and fail when
applied to systems with different parameters, external forces, or boundary
conditions. We propose a model-based meta-learning method called DyAd which can
generalize across heterogeneous domains by partitioning them into different
tasks. DyAd has two parts: an encoder which infers the time-invariant hidden
features of the task with weak supervision, and a forecaster which learns the
shared dynamics of the …

arxiv dynamics forecasting inference learning lg meta meta-learning