Probabilistic physics-informed machine learning for dynamic systems

Abhinav Subramanian and Sankaran Mahadevan

Reliability Engineering and System Safety, 2023, vol. 230, issue C

Abstract: This paper develops a physics-informed machine learning approach for response prediction in dynamic systems, by augmenting a physics-based model with a machine learning model for model error; both models are probabilistic. The physics-based dynamic system model has discrepancy in the predicted output (due to incomplete physics or model form error in the physics-based model) when compared against observation data. The model form error is quantified using Bayesian state estimation with observation data, and a probabilistic machine learning model is trained to predict the output discrepancy for untrained inputs. Two different computational options are developed to implement this overall approach, with different levels of sophistication and computational effort. The methods are developed for problems with stationary and non-stationary, and Gaussian and non-Gaussian random process inputs. Strategies for computational effort reduction at different stages, such as model training, model error quantification, and reliability prediction, are discussed. The proposed approach is demonstrated using two numerical illustrations: (a) a deep beam subjected to dynamic loading (a single physics system), and (b) hypersonic flow over a flexible aircraft panel (a multi-physics system).

Keywords: Physics-informed machine learning; Model error; Probabilistic machine learning; Bayesian state estimation; Information fusion (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005142

DOI: 10.1016/j.ress.2022.108899

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