Bayesian optimized physics-informed neural network for estimating wave propagation velocities
arxiv(2023)
摘要
In this paper, we propose a novel inverse parameter estimation approach
called Bayesian optimized physics-informed neural network (BOPINN). In this
study, a PINN solves the partial differential equation (PDE), whereas Bayesian
optimization (BO) estimates its parameter. The proposed BOPINN estimates wave
velocity associated with wave propagation PDE using a single snapshot
observation. An objective function for BO is defined as the mean squared error
(MSE) between the surrogate displacement field and snapshot observation. The
inverse estimation capability of the proposed approach is tested in three
different isotropic media with different wave velocities. From the obtained
results, we have observed that BOPINN can accurately estimate wave velocities
with lower MSE, even in the presence of noisy conditions. The proposed
algorithm shows robust predictions in limited iterations across different runs.
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