  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.
