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.

翻译：数据驱动的动力学预测在环境偏移下常失效，而传统微调方法对硬件受限或数据匮乏的应用场景计算成本过高。我们提出DynaDiff生成式元学习框架，将范式从基于梯度的调优或调制转变为直接权重空间生成。具体而言，我们首先将专家权重抽象为新型权重图，利用多头注意力机制显式捕获权重内部的拓扑耦合关系；其次设计功能损失函数，确保生成模型在物理行为上与专家模型保持一致性；最后构建动力学感知提示器，从观测序列中提取跨域物理与频谱特征以条件化扩散模型。实验表明，DynaDiff在竞争性基线方法基础上将平均预测精度提升10.78%。此外，通过预构建专家预测器模型库，我们将微调开销分摊为一次性离线成本，显著提升新环境下的部署效率。