Inverse design optimization aims to infer system parameters from observed solutions, posing critical challenges across domains such as semiconductor manufacturing, structural engineering, materials science, and fluid dynamics. The lack of explicit mathematical representations in many systems complicates this process and makes the first order optimization impossible. Mainstream approaches, including generative AI and Bayesian optimization, address these challenges but have limitations. Generative AI is computationally expensive, while Bayesian optimization, relying on surrogate models, suffers from scalability, sensitivity to priors, and noise issues, often leading to suboptimal solutions. This paper introduces Deep Physics Prior (DPP), a novel method enabling first-order gradient-based inverse optimization with surrogate machine learning models. By leveraging pretrained auxiliary Neural Operators, DPP enforces prior distribution constraints to ensure robust and meaningful solutions. This approach is particularly effective when prior data and observation distributions are unknown.

该研究解决了逆向设计优化中存在的从观测解决方案推断系统参数的难题，特别是在许多系统缺乏明确数学表征的情况下。提出了一种名为深度物理先验（DPP）的新方法，通过预训练的辅助神经算子实现了基于梯度的第一阶逆向优化，确保可靠且有意义的解决方案。这一方法在先验数据和观测分布未知的情况下尤其有效，具有显著的应用潜力。

第一阶逆向优化的深度物理先验方法