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1. Introductory physics lab instructors’ perspectives on measurement uncertainty

   https://doi.org/10.1103/PhysRevPhysEducRes.17.010133  

   Pollard, Benjamin; Hobbs, Robert; Henderson, Rachel; Caballero, Marcos D.; Lewandowski, H. J. (May 2021, Physical Review Physics Education Research)

   null (Ed.)
2. Physics-Informed Uncertainty Quantification in Modeling of Machining-Induced Residual Stress

   https://doi.org/10.1016/j.procir.2023.03.025  

   Hasan, Md Mehedi; Schoop, Julius (January 2023, Procedia CIRP)

   Machining processes involve various sources of uncertainty which lead to inaccurate interpretation of results in the surface integrity of machined products. This work presents a physics-informed, data-driven modeling framework for achieving comprehensive uncertainty quantification (UQ) of the impact of process and material variability on machining-induced residual stress (RS). Uncertainty due to the variation in bulk material properties and model input parameters in machining are considered. Preliminary results showed that variations in calibration parameters have a substantial effect on modeling RS, while the variation in material properties has a smaller effect. Further research directions for UQ in machining are also outlined. 

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3. Uncertainty Quantification of Car-following Behaviors: Physics-Informed Generative Adversarial Networks

   Zhaobin Mo; Xuan Di (August 2022, the 28th ACM SIGKDD in conjunction with the 11th International Workshop on Urban Computing (UrbComp2022))

   http://urban-computing.com/urbcomp2022/file/UrbComp2022_paper_3574.pdf 

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4. Accounting for Physics Uncertainty in UltrasonicWave Propagation using Deep Learning

   Khurjekar, Ishan; Harley, Joel B. (December 2019, Machine Learning and the Physical Sciences Workshop at the Conference on Neural Information Processing Systems (NeurIPS))

   Ultrasonic guided waves are commonly used to localize structural damage in infrastructures such as buildings, airplanes, bridges. Damage localization can be viewed as an inverse problem. Physical model based techniques are popular for guided wave based damage localization. The performance of these techniques depend on the degree of faithfulness with which the physical model describes wave propagation. External factors such as environmental variations and random noise are a source of uncertainty in wave propagation. The physical modeling of uncertainty in an inverse problem is still a challenging problem. In this work, we propose a deep learning based model for robust damage localization in presence of uncertainty. Wave data with uncertainty is simulated to reflect variations due to external factors and Gaussian noise is added to reflect random noise in the environment. After evaluating the localization error on test data with uncertainty, we observe that the deep learning model trained with uncertainty can learn robust representations. The approach shows the potential for dealing with uncertainty in physical science problems using deep learning models. 

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5. Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology

   https://doi.org/10.1098/rsta.2024.0221  

   De_Florio, Mario; Zou, Zongren; Schiavazzi, Daniele E; Karniadakis, George Em (March 2025, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)

   When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model is accurate. Possible sources include irreduciblealeatoricuncertainty due to noise in the data,epistemicuncertainty induced by insufficient data or inadequate parameterization andmodel-formuncertainty related to the use of misspecified model equations. In addition, recently proposed approaches provide flexible ways to combine information from data with full or partial satisfaction of equations that typically encode physical principles. Physics-based regularization interacts in non-trivial ways with aleatoric, epistemic and model-form uncertainty and their combination, and a better understanding of this interaction is needed to improve the predictive performance of physics-informed digital twins that operate under real conditions. To better understand this interaction, with a specific focus on biological and physiological models, this study investigates the decomposition of total uncertainty in the estimation of states and parameters of a differential system simulated with MC X-TFC, a new physics-informed approach for uncertainty quantification based on random projections and Monte Carlo sampling. After an introductory comparison between approaches for physics-informed estimation, MC X-TFC is applied to a six-compartment stiff ODE system, the CVSim-6 model, developed in the context of human physiology. The system is first analysed by progressively removing data while estimating an increasing number of parameters, and subsequently by investigating total uncertainty under model-form misspecification of nonlinear resistance in the pulmonary compartment. In particular, we focus on the interaction between the formulation of the discrepancy term and quantification of model-form uncertainty, and show how additional physics can help in the estimation process. The method demonstrates robustness and efficiency in estimating unknown states and parameters, even with limited, sparse and noisy data. It also offers great flexibility in integrating data with physics for improved estimation, even in cases of model misspecification. This article is part of the theme issue ‘Uncertainty quantification for healthcare and biological systems (Part 1)’. 

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