We present a non-intrusive version of the index-aware learning framework introduced in arXiv:2309.00958. Index-aware learning itself is an approach for learning the time and parameter dependent solutions of differential-algebraic equations (DAEs), in particular those of electrical circuits. A key feature of the approach is that it ensures the learned solutions to remain physics-consistent, i.e.\ inherent constraints of the solution, such as e.g.\ Kirchhoff's laws, remain fulfilled. In general, this is achieved by leveraging a decoupling of the DAE into its differential and algebraic parts, while the non-intrusive version of the approach additionally relies on results from arXiv:2604.20475 and arXiv:2107.07755. We illustrate the overall workflow and compare the non-intrusive and intrusive versions using a buck converter as an example.

翻译：我们提出了arXiv:2309.00958中引入的索引感知学习框架的非侵入式版本。索引感知学习本身是一种学习微分代数方程（DAE）特别是电路方程中依赖于时间和参数的解的方法。该方法的一个关键特性是确保学习到的解保持物理一致性，即解的固有约束（例如基尔霍夫定律）始终得到满足。通常，这是通过将DAE解耦为微分部分和代数部分来实现的，而该方法的非侵入式版本还依赖于arXiv:2604.20475和arXiv:2107.07755的结果。我们以降压转换器为例，说明了整体工作流程，并比较了非侵入式和侵入式版本。