Viability of Tensor Train Methods for Geophysical Fluid Dynamics
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918532365680640 |
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| author | Lilly, Jeremy DeSantis, Derek Petersen, Mark R. |
| author_facet | Lilly, Jeremy DeSantis, Derek Petersen, Mark R. |
| contents | Tensor train (TT) methods have recently gained popularity for accelerating the solving of systems of PDEs. Here, we evaluate the performance of TT methods in the context of geophysical fluid dynamics (GFD) using the shallow water equations and a discretization scheme employed by the ocean component of the Energy Exascale Earth System Model (E3SM). Through a suite of four test cases of increasing complexity, we evaluate TT methods in terms of how much TT is able to compress the model state, the error incurred by the TT approximation, and the speedup obtained by TT versus an optimal standard non-TT implementation in a representative subproblem. We show that though TT is able to effectively compress and speed up simple flows, it struggles to efficiently represent more complex states that are common in realistic GFD applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2606_00055 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Viability of Tensor Train Methods for Geophysical Fluid Dynamics Lilly, Jeremy DeSantis, Derek Petersen, Mark R. Fluid Dynamics Numerical Analysis Atmospheric and Oceanic Physics Tensor train (TT) methods have recently gained popularity for accelerating the solving of systems of PDEs. Here, we evaluate the performance of TT methods in the context of geophysical fluid dynamics (GFD) using the shallow water equations and a discretization scheme employed by the ocean component of the Energy Exascale Earth System Model (E3SM). Through a suite of four test cases of increasing complexity, we evaluate TT methods in terms of how much TT is able to compress the model state, the error incurred by the TT approximation, and the speedup obtained by TT versus an optimal standard non-TT implementation in a representative subproblem. We show that though TT is able to effectively compress and speed up simple flows, it struggles to efficiently represent more complex states that are common in realistic GFD applications. |
| title | Viability of Tensor Train Methods for Geophysical Fluid Dynamics |
| topic | Fluid Dynamics Numerical Analysis Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2606.00055 |