Equation identification for fluid flows via physics-informed neural networks
This work addresses the need for better assessment of PINN methods in scientific machine learning, though it is incremental as it focuses on benchmarking and optimization improvements rather than a breakthrough.
The authors tackled the problem of evaluating physics-informed neural networks (PINNs) for inverse problems across various governing equations by introducing a new benchmark based on the 2D Burgers' equation with rotational flow, showing that a novel alternating first- and second-order optimization strategy outperforms typical first-order methods for parameter estimation.
Scientific machine learning (SciML) methods such as physics-informed neural networks (PINNs) are used to estimate parameters of interest from governing equations and small quantities of data. However, there has been little work in assessing how well PINNs perform for inverse problems across wide ranges of governing equations across the mathematical sciences. We present a new and challenging benchmark problem for inverse PINNs based on a parametric sweep of the 2D Burgers' equation with rotational flow. We show that a novel strategy that alternates between first- and second-order optimization proves superior to typical first-order strategies for estimating parameters. In addition, we propose a novel data-driven method to characterize PINN effectiveness in the inverse setting. PINNs' physics-informed regularization enables them to leverage small quantities of data more efficiently than the data-driven baseline. However, both PINNs and the baseline can fail to recover parameters for highly inviscid flows, motivating the need for further development of PINN methods.