Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach

arXiv:2404.05768v1 Announce Type: new
Abstract: Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper's advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration …

abstract advanced algorithms architecture arxiv cs.lg deep learning development dynamics focus fourier hyperparameter learn modeling networks neural networks ocean ocean modeling operators optimization physics.ao-ph processes search search algorithms stat.ml training type