发表机构
UnivEvry, Université Paris-Saclay; ENPC, EDF R&D, Institut Polytechnique de Paris（巴黎-萨克雷大学（埃夫里）; 巴黎综合理工学院，巴黎桥路学院，法国电力公司研发部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于物理感知潜空间的神经网络框架，耦合降阶代理与变分参数估计，在计算流体动力学基准实验中，其可提升参数校准鲁棒性并降低误差与变异性。
AI 中文摘要
参数化动力系统的正向与逆向建模不仅需要代理模型具备准确的状态预测能力，还需能为参数校准提供有效信息。然而，将深度学习降阶代理与变分参数估计耦合的端到端可微系统框架仍未得到充分发展。本研究提出一种基于神经网络的物理感知潜空间框架，用于降阶正向建模与变分参数估计。该基于自动编码器的方法生成可微代理，通过潜表征将物理参数映射至预测流场；离线训练阶段采用可观测监督，促使潜变量保留与系统参数相关的信息，在线逆向问题则通过代理诱导的观测算子在参数空间求解。在两个计算流体动力学基准上的评估结果显示，仅重建精度不足以支撑逆向建模，原因在于其缺乏变分参数校准所需的端到端可微性或物理感知。定量潜空间分析进一步表明，可观测监督提升了潜表征的案例级可分性与时间组织性。在含噪声、低分辨率、随机掩码及分块部分观测等真实测量设置下的实验，验证了所提框架的鲁棒性，且与标准代理模型相比，其通常能降低校准误差与变异性。
英文摘要
Forward and inverse modeling of parametric dynamical systems requires surrogate models that are not only accurate for state prediction, but also informative for parameter calibration. However, a systematic end-to-end differentiable formulation for coupling deep-learning-based reduced-order surrogates with variational parameter estimation remains underdeveloped. In this work, we introduce a physics-aware neural-network-based latent-space framework for reduced-order forward modeling and variational parameter estimation. The proposed autoencoder-based approach yields a differentiable surrogate that maps physical parameters to predicted flow fields through a latent representation. The observable supervision is used during offline training to encourage the latent variables to retain information correlated with system parameters, while the online inverse problem is solved in the parameter space through the surrogate-induced observation operator. The method is evaluated on two computational-fluid-dynamics benchmarks. The results show that reconstruction accuracy alone is insufficient for inverse modeling, owing to the lack of end-to-end differentiability or physics awareness for variational parameter calibration. Quantitative latent-space analysis further shows that observable supervision improves case-level separability and temporal organization of latent representations. Experiments with realistic measurement settings, including noisy, low-resolution, randomly masked, and block-wise partial observations, demonstrate the robustness of the proposed framework and show that it generally reduces calibration error and variability compared with the standard surrogate models.
