Generative Adaptation of Dynamics to Environmental Shifts via Weight-space Diffusion
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915977449439232 |
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| author | Li, Ruikun Wang, Huandong Ding, Jingtao Yuan, Yuan Liao, Qingmin Li, Yong |
| author_facet | Li, Ruikun Wang, Huandong Ding, Jingtao Yuan, Yuan Liao, Qingmin Li, Yong |
| contents | Data-driven dynamics prediction often fails under environmental shifts, while traditional fine-tuning remains computationally prohibitive for hardware-constrained or data-scarce applications. We propose DynaDiff, a generative meta-learning framework that transitions the paradigm from gradient-based tuning or modulation to direct weight-space generation. Specifically, we first abstract expert weights as novel weight graphs, utilizing multi-head attention to explicitly capture topological coupling within weights. Subsequently, we design a functional loss to ensure that the generated models achieve consistency with expert models in physical behavior. Finally, we develop a dynamics-informed prompter that extracts cross-domain physical and spectral features from observation sequences to condition the diffusion model. Experiments demonstrate that DynaDiff boosts average prediction accuracy by 10.78% over competitive baselines. Furthermore, by pre-constructing a model zoo of expert predictors, we amortize the fine-tuning overhead into a one-time offline cost, significantly boosting deployment efficiency in new environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Adaptation of Dynamics to Environmental Shifts via Weight-space Diffusion Li, Ruikun Wang, Huandong Ding, Jingtao Yuan, Yuan Liao, Qingmin Li, Yong Computational Engineering, Finance, and Science Data-driven dynamics prediction often fails under environmental shifts, while traditional fine-tuning remains computationally prohibitive for hardware-constrained or data-scarce applications. We propose DynaDiff, a generative meta-learning framework that transitions the paradigm from gradient-based tuning or modulation to direct weight-space generation. Specifically, we first abstract expert weights as novel weight graphs, utilizing multi-head attention to explicitly capture topological coupling within weights. Subsequently, we design a functional loss to ensure that the generated models achieve consistency with expert models in physical behavior. Finally, we develop a dynamics-informed prompter that extracts cross-domain physical and spectral features from observation sequences to condition the diffusion model. Experiments demonstrate that DynaDiff boosts average prediction accuracy by 10.78% over competitive baselines. Furthermore, by pre-constructing a model zoo of expert predictors, we amortize the fine-tuning overhead into a one-time offline cost, significantly boosting deployment efficiency in new environments. |
| title | Generative Adaptation of Dynamics to Environmental Shifts via Weight-space Diffusion |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2505.13919 |