Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory
Igor Kavrakov, Gledson Rodrigo Tondo, Guido Morgenthal

TL;DR
This paper introduces a Bayesian physics-informed Gaussian Process method for stochastic inference of plate bending, integrating heterogeneous data and Kirchhoff-Love theory to estimate structural parameters and responses.
Contribution
It develops a novel probabilistic framework combining Gaussian Processes with Kirchhoff-Love theory for plates, enabling uncertainty quantification from diverse sensor data.
Findings
Successfully inferred plate rigidity and responses from noisy measurements.
Demonstrated applicability on simply supported and fixed plates under different loads.
Enabled integration of heterogeneous sensor data for structural health monitoring.
Abstract
Advancements in machine learning and an abundance of structural monitoring data have inspired the integration of mechanical models with probabilistic models to identify a structure's state and quantify the uncertainty of its physical parameters and response. In this paper, we propose an inference methodology for classical Kirchhoff-Love plates via physics-informed Gaussian Processes (GP). A probabilistic model is formulated as a multi-output GP by placing a GP prior on the deflection and deriving the covariance function using the linear differential operators of the plate governing equations. The posteriors of the flexural rigidity, hyperparameters, and plate response are inferred in a Bayesian manner using Markov chain Monte Carlo (MCMC) sampling from noisy measurements. We demonstrate the applicability with two examples: a simply supported plate subjected to a sinusoidal load and a…
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Taxonomy
TopicsModel Reduction and Neural Networks · Probabilistic and Robust Engineering Design · Advanced Measurement and Metrology Techniques
