ATLAS-NN: Adaptive Transfer Learnable Symplectic-aware Neural Network for Long-Time Hamiltonian Dynamics

Abstract

Modeling Hamiltonian systems over long temporal intervals remains a significant challenge due to intrinsic multiscale structures and rapid nonlinear transitions. While Hamiltonian Neural Networks (HNNs) incorporate geometric invariants to improve stability, they typically rely on a fixed, externally prescribed temporal structure. This lack of adaptability often leads to accumulated phase errors and degraded accuracy in systems with heterogeneous temporal scales. To address these limitations, we put forward the Adaptive Transfer Learnable Symplectic-aware Neural Network (ATLAS-NN). Our framework augments the HNN architecture with a learnable temporal scaling mechanism that parametrize a nonlinear mapping of time, automatically adapting to the system's intrinsic complexity. We propose a two-stage transfer learning strategy: the model is first trained on a short-time \textit{source} interval to identify the Hamiltonian structure and optimal temporal reparameterization; the learned scaling function is then frozen and transferred to an extended \textit{target} interval for fine-tuning. Numerical experiments on nonlinear oscillators and the chaotic Hénon--Heiles system demonstrate that ATLAS-NN provides a more efficient alternative to standard HNNs and traditional symplectic integrators, yielding nearly an order of magnitude reduction in long-time prediction error.