MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912869371609088 |
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| author | Vaidhyanathan, Pranav Papatheodorou, Aristotelis Mitchison, Mark T. Ares, Natalia Havoutis, Ioannis |
| author_facet | Vaidhyanathan, Pranav Papatheodorou, Aristotelis Mitchison, Mark T. Ares, Natalia Havoutis, Ioannis |
| contents | Scalable and generalizable physics-aware deep learning has long been considered a significant challenge with various applications across diverse domains ranging from robotics to molecular dynamics. Central to almost all physical systems are symplectic forms, the geometric backbone that underpins fundamental invariants like energy and momentum. In this work, we introduce a novel deep learning framework, MetaSym. In particular, MetaSym combines a strong symplectic inductive bias obtained from a symplectic encoder, and an autoregressive decoder with meta-attention. This principled design ensures that core physical invariants remain intact, while allowing flexible, data efficient adaptation to system heterogeneities. We benchmark MetaSym with highly varied and realistic datasets, such as a high-dimensional spring-mesh system Otness et al. (2021), an open quantum system with dissipation and measurement backaction, and robotics-inspired quadrotor dynamics. Crucially, we fine-tune and deploy MetaSym on real-world quadrotor data, demonstrating robustness to sensor noise and real-world uncertainty. Across all tasks, MetaSym achieves superior few-shot adaptation and outperforms larger state-of-the-art (SOTA) models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16667 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence Vaidhyanathan, Pranav Papatheodorou, Aristotelis Mitchison, Mark T. Ares, Natalia Havoutis, Ioannis Machine Learning Robotics Computational Physics Quantum Physics Scalable and generalizable physics-aware deep learning has long been considered a significant challenge with various applications across diverse domains ranging from robotics to molecular dynamics. Central to almost all physical systems are symplectic forms, the geometric backbone that underpins fundamental invariants like energy and momentum. In this work, we introduce a novel deep learning framework, MetaSym. In particular, MetaSym combines a strong symplectic inductive bias obtained from a symplectic encoder, and an autoregressive decoder with meta-attention. This principled design ensures that core physical invariants remain intact, while allowing flexible, data efficient adaptation to system heterogeneities. We benchmark MetaSym with highly varied and realistic datasets, such as a high-dimensional spring-mesh system Otness et al. (2021), an open quantum system with dissipation and measurement backaction, and robotics-inspired quadrotor dynamics. Crucially, we fine-tune and deploy MetaSym on real-world quadrotor data, demonstrating robustness to sensor noise and real-world uncertainty. Across all tasks, MetaSym achieves superior few-shot adaptation and outperforms larger state-of-the-art (SOTA) models. |
| title | MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence |
| topic | Machine Learning Robotics Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2502.16667 |