Fill the gaps: continuous in time interpolation of fluid dynamical simulations
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915611955691520 |
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| author | Pronk, Jonas Porth, Oliver Davelaar, Jordy |
| author_facet | Pronk, Jonas Porth, Oliver Davelaar, Jordy |
| contents | Flexible and accurate interpolation schemes using machine learning could be of great benefit for many use-cases in numerical simulations and post-processing, such as temporal upsampling or storage reduction. In this work, we adapt the physics-informed token transformer (PITT) network for multi-channel data and couple it with Fourier neural operator (FNO). The resulting PITT FNO network is trained for interpolation tasks on a dataset governed by the Euler equations. We compare the performance of our machine learning model with a linear interpolation baseline and show that it requires $\sim6-10$ times less data to achieve the same mean square error of the interpolated quantities. Additionally, PITT FNO has excellent mass and energy conservation as a result of its physics-informed nature. We further discuss the ability of the network to recover fine detail using a spectral analysis. Our results suggest that loss of fine details is related to the decreasing correlation time of the data with increasing Fourier mode which cannot be resolved by simply increasing Fourier mode truncation in FNO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08239 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fill the gaps: continuous in time interpolation of fluid dynamical simulations Pronk, Jonas Porth, Oliver Davelaar, Jordy High Energy Astrophysical Phenomena Computational Physics Flexible and accurate interpolation schemes using machine learning could be of great benefit for many use-cases in numerical simulations and post-processing, such as temporal upsampling or storage reduction. In this work, we adapt the physics-informed token transformer (PITT) network for multi-channel data and couple it with Fourier neural operator (FNO). The resulting PITT FNO network is trained for interpolation tasks on a dataset governed by the Euler equations. We compare the performance of our machine learning model with a linear interpolation baseline and show that it requires $\sim6-10$ times less data to achieve the same mean square error of the interpolated quantities. Additionally, PITT FNO has excellent mass and energy conservation as a result of its physics-informed nature. We further discuss the ability of the network to recover fine detail using a spectral analysis. Our results suggest that loss of fine details is related to the decreasing correlation time of the data with increasing Fourier mode which cannot be resolved by simply increasing Fourier mode truncation in FNO. |
| title | Fill the gaps: continuous in time interpolation of fluid dynamical simulations |
| topic | High Energy Astrophysical Phenomena Computational Physics |
| url | https://arxiv.org/abs/2511.08239 |