  Physics-Informed Neural Networks (PINNs) have recently been proposed to solve
scientific and engineering problems, where physical laws are introduced into
neural networks as prior knowledge. With the embedded physical laws, PINNs
enable the estimation of critical parameters, which are unobservable via
physical tools, through observable variables. For example, Power Electronic
Converters (PECs) are essential building blocks for the green energy
transition. PINNs have been applied to estimate the capacitance, which is
unobservable during PEC operations, using current and voltage, which can be
observed easily during operations. The estimated capacitance facilitates
self-diagnostics of PECs. Existing PINNs are often manually designed, which is
time-consuming and may lead to suboptimal performance due to a large number of
design choices for neural network architectures and hyperparameters. In
addition, PINNs are often deployed on different physical devices, e.g., PECs,
with limited and varying resources. Therefore, it requires designing different
PINN models under different resource constraints, making it an even more
challenging task for manual design. To contend with the challenges, we propose
Automated Physics-Informed Neural Networks (AutoPINN), a framework that enables
the automated design of PINNs by combining AutoML and PINNs. Specifically, we
first tailor a search space that allows finding high-accuracy PINNs for PEC
internal parameter estimation. We then propose a resource-aware search strategy
to explore the search space to find the best PINN model under different
resource constraints. We experimentally demonstrate that AutoPINN is able to
find more accurate PINN models than human-designed, state-of-the-art PINN
models using fewer resources.
