Rollout-LaSDI: Enhancing the long-term accuracy of Latent Space Dynamics
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915488315998208 |
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| author | Stephany, Robert Choi, Youngsoo |
| author_facet | Stephany, Robert Choi, Youngsoo |
| contents | Solving complex partial differential equations is vital in the physical sciences, but often requires computationally expensive numerical methods. Reduced-order models (ROMs) address this by exploiting dimensionality reduction to create fast approximations. While modern ROMs can solve parameterized families of PDEs, their predictive power degrades over long time horizons. We address this by (1) introducing a flexible, high-order, yet inexpensive finite-difference scheme and (2) proposing a Rollout loss that trains ROMs to make accurate predictions over arbitrary time horizons. We demonstrate our approach on the 2D Burgers equation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08191 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rollout-LaSDI: Enhancing the long-term accuracy of Latent Space Dynamics Stephany, Robert Choi, Youngsoo Machine Learning Solving complex partial differential equations is vital in the physical sciences, but often requires computationally expensive numerical methods. Reduced-order models (ROMs) address this by exploiting dimensionality reduction to create fast approximations. While modern ROMs can solve parameterized families of PDEs, their predictive power degrades over long time horizons. We address this by (1) introducing a flexible, high-order, yet inexpensive finite-difference scheme and (2) proposing a Rollout loss that trains ROMs to make accurate predictions over arbitrary time horizons. We demonstrate our approach on the 2D Burgers equation. |
| title | Rollout-LaSDI: Enhancing the long-term accuracy of Latent Space Dynamics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.08191 |