Inference-Decoupled Physics Manifold Regularization for Sequential Degradation Prognostics

Integrating domain-specific physical priors into deep sequence models for degradation prognostics is fundamentally challenged by a causal availability paradox: informative thermodynamic and kinetic descriptors are retrospective, post-cycle aggregated quantities that are inaccessible during real-time streaming inference. Directly feeding these privileged descriptors into model inputs introduces non-causal dependencies that prevent online deployment, while unconstrained sequence backbones systematically fail to capture underlying phase-transition dynamics. To resolve this tension, we propose Inference-Decoupled Physics Manifold Regularization (ID-PMR). ID-PMR constructs an auxiliary manifold regression task strictly during training to regularize latent geometric representations via uncertainty-weighted optimization, and completely severs this auxiliary branch during inference—achieving zero runtime computational overhead, zero parameter growth, and zero reliance on retrospective features. Across 76 experimental configurations on four heterogeneous battery benchmarks (NASA, MATR, HUST, XJTU), ID-PMR consistently outperforms unconstrained baselines by up to 78.5% in test RMSE. Closed-loop probing confirms that latent physical interpretability recovers from near-zero to a mean R2 of 0.83. Furthermore, extensive ablations characterize a continuous manifold constraint spectrum and show that source-domain trajectory diversity is strongly associated with out-of-distribution transferability. ID-PMR provides a principled, causally compliant paradigm for infusing high-order physical priors into real-time sequence architectures.