92 citations · 92 across the 5 of their papers we have counts for
6 papers
Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data
Guang Zhao, Xihaier Luo, Sean McCue +5
Implicit neural representations (INRs) offer a powerful framework for modeling high-dimensional physical fields, but their training remains inefficient because evaluating dense coo…
Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates
Yixuan Sun, Elizabeth Cucuzzella, Steven Brus +6
Numerical models of the ocean and ice sheets are crucial for understanding and simulating the impact of greenhouse gases on the global climate. Oceanic processes affect phenomena s…
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach
Yixuan Sun, Ololade Sowunmi, Romain Egele +3
Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage the advanced search algorithms for multiobjectiv…
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
Yixuan Sun, Elizabeth Cucuzzella, Steven Brus +5
Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurrica…
A new hybrid mass-flux/ high-order turbulence closure for ocean vertical mixing
Amrapalli Garanaik, Filipe Pereira, Katherine Smith +4
While various parameterizations of vertical turbulent fluxes at different levels of complexity have been proposed, each has its own limitations. For example, simple first-order clo…
The KPP boundary layer scheme: revisiting its formulation and benchmarking one-dimensional ocean simulations relative to LES
Luke P Van Roekel, Alistair J. Adcroft, Gokhan Danabasoglu +7
We evaluate the Community ocean Vertical Mixing (CVMix) project version of the K-profile parameterization (KPP). For this purpose, one-dimensional KPP simulations are compared acro…