HOPE: An Automatically Differentiable High-Order Non-Oscillatory Finite-Volume Shallow-Water Dynamic Core
Lilong Zhou

TL;DR
HOPE is a high-order, differentiable finite volume shallow water model built on PyTorch, offering non-oscillatory solutions, mass conservation, and GPU scalability, suitable for accurate and efficient geophysical simulations.
Contribution
This work introduces HOPE, a novel high-order, automatically differentiable shallow water dynamic core with advanced reconstruction and boundary techniques, optimized for GPU architecture.
Findings
Achieves near double-precision accuracy on coarse grids in steady geostrophic flow
Maintains Rossby-Haurwitz wave stability over 100 days
Effectively suppresses non-physical oscillations in dam break tests
Abstract
An automatically differentiable, high-order non-oscillatory finite volume shallow water dynamic core has been constructed on a cubed sphere grid. This dynamic core has four advantageous properties: high order accuracy, essential non-oscillation, mass conservation, and scalability. Besides, the code development is based on PyTorch, enabling the model to run seamlessly on both CPU and GPU, while naturally possessing the capability of automatic differentiation. We named the new dynamic core as High Order Prediction Environment (HOPE). The spatial reconstruction is based on the two-dimensional tensor product polynomial (TPP) and the genuine two-dimensional Weighted Essentially Non-Oscillatory scheme. A novel panel boundary approach ensures that the accuracy can reach arbitrary order. These algorithms have very high degree of compatibility with GPU architecture, allowing the computational…
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Taxonomy
TopicsCoastal and Marine Dynamics · Lattice Boltzmann Simulation Studies · Methane Hydrates and Related Phenomena
