this project contains all the codes used for the paper: " Self-Validated Physics-Embedding Network: A General Framework for Inverse Modelling", a paper talks about using neural optimzation on forward physical models to solve inverse engineering problem for the purpose of covering the unrobustness of inverse ML model.

Physics-based inverse modeling techniques are typically restricted to particular research fields, whereas popular machine-learning-based ones are too data-dependent to guarantee the physical compatibility of the solution. In this paper, Self-Validated Physics-Embedding Network (SVPEN), a general neural network framework for inverse modeling is proposed. As its name suggests, the embedded physical forward model ensures that any solution that successfully passes its validation is physically reasonable. SVPEN operates in two modes: (a) the inverse function mode offers rapid state estimation as conventional supervised learning, and (b) the optimization mode offers a way to iteratively correct estimations that fail the validation process. Furthermore, the optimization mode provides SVPEN with reconfigurability i.e., replacing components like neural networks, physical models, and error calculations at will to solve a series of distinct inverse problems without pretraining. More than ten case studies in two highly nonlinear and entirely distinct applications: molecular absorption spectroscopy and Turbofan cycle analysis, demonstrate the generality, physical reliability, and reconfigurability of SVPEN. More importantly, SVPEN offers a solid foundation to use existing physical models within the context of AI, so as to striking a balance between data-driven and physics-driven models.

the project contains two folders which respectively corresponds to the two applications mentioned in the paper : spectroscopy and gasturbine engine. The forward models used in these two applications are respectively based on [radis](radis.misc.config module — RADIS 0.13.1 documentation) and [Huracan](GitHub - alopezrivera/huracan: Open source, 0-dimensional, object-oriented airbreathing engine modelling package for preliminary analysis and design of airbreathing engines, divulgation and educational purposes.) . The structure is pretty simple, and one can directly download and run the framework.py.

one can cite the paper by following bibtex.

@article{kang2022self, title={Self-Validated Physics-Embedding Network: A General Framework for Inverse Modelling}, author={Kang, Ruiyuan and Kyritsis, Dimitrios C and Liatsis, Panos}, journal={arXiv preprint arXiv:2210.06071}, year={2022} }