Self-Validated Physics-Embedding Network: A General Framework for Inverse Modelling

• URL: http://arxiv.org/abs/2210.06071v1
• Date: Wed, 12 Oct 2022 10:31:36 GMT
• Title: Self-Validated Physics-Embedding Network: A General Framework for Inverse Modelling
• Authors: Ruiyuan Kang, Dimitrios C. Kyritsis, Panos Liatsis
• Abstract summary: Self-Embedding Physics-Embedding Network (SVPEN) is a general neural network framework for inverse modeling. The embedded physical forward model ensures that any solution that successfully passes its validation is physically reasonable. More than ten case studies in two highly nonlinear and entirely distinct applications are presented.
• Score: 2.449329947677678
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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