On the Preprocessing of Physics-informed Neural Networks: How to Better Utilize Data in Fluid Mechanics
arxiv(2024)
摘要
Physics-Informed Neural Networks (PINNs) serve as a flexible alternative for
tackling forward and inverse problems in differential equations, displaying
impressive advancements in diverse areas of applied mathematics. Despite
integrating both data and underlying physics to enrich the neural network's
understanding, concerns regarding the effectiveness and practicality of PINNs
persist. Over the past few years, extensive efforts in the current literature
have been made to enhance this evolving method, by drawing inspiration from
both machine learning algorithms and numerical methods. Despite notable
progressions in PINNs algorithms, the important and fundamental field of data
preprocessing remain unexplored, limiting the applications of PINNs especially
in solving inverse problems. Therefore in this paper, a concise yet potent data
preprocessing method focusing on data normalization was proposed. By applying a
linear transformation to both the data and corresponding equations
concurrently, the normalized PINNs approach was evaluated on the task of
reconstructing flow fields in three turbulent cases. The results, both
qualitatively and quantitatively, illustrate that by adhering to the data
preprocessing procedure, PINNs can robustly achieve higher prediction accuracy
for all flow quantities through the entire training process, distinctly
improving the utilization of limited training data. The proposed normalization
method requires zero extra computational cost. Though only verified in
Navier-Stokes (NS) equations, this method holds potential for application to
various other equations.
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