Summary of Stochastic Inference Of Plate Bending From Heterogeneous Data: Physics-informed Gaussian Processes Via Kirchhoff-love Theory, by Igor Kavrakov et al.
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory
by Igor Kavrakov, Gledson Rodrigo Tondo, Guido Morgenthal
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Analysis, Statistics and Probability (physics.data-an)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative approach to identifying a structure’s state and quantifying its physical parameters and response using probabilistic models and machine learning. The authors integrate mechanical models with Gaussian Processes (GP) to create a physics-informed GP model that captures the uncertainty of plate response. By applying Markov chain Monte Carlo (MCMC) sampling, the method infers the flexural rigidity, hyperparameters, and plate response from noisy measurements. The authors demonstrate this approach on two examples: a simply supported plate under a sinusoidal load and a fixed plate under a uniform load. This methodology has potential applications in structural health monitoring and uncertainty quantification of plate-like structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to help predict the state of buildings and bridges. It combines ideas from physics and math to create a new model that can handle noisy data and uncertainty. The authors show how this approach works by testing it on two different scenarios: one where a plate is bent under a wave-like force, and another where a plate is held in place under a steady force. This technique has the potential to improve our ability to monitor buildings and bridges and make more accurate predictions about their behavior. |
Keywords
» Artificial intelligence » Machine learning
