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1. Role of physics in physics-informed machine learning

   https://doi.org/10.1615/JMachLearnModelComput.2024053170  

   Chandra, A; Bakarji, J; Tartakovsky, D M (January 2024, Journal of Machine Learning for Modeling and Computing)

   Physical systems are characterized by inherent symmetries, one of which is encapsulated in theunits of their parameters and system states. These symmetries enable a lossless order-reduction, e.g.,via dimensional analysis based on the Buckingham theorem. Despite the latter's benefits, machinelearning (ML) strategies for the discovery of constitutive laws seldom subject experimental and/ornumerical data to dimensional analysis. We demonstrate the potential of dimensional analysis to significantlyenhance the interpretability and generalizability of ML-discovered secondary laws. Ournumerical experiments with creeping fluid flow past solid ellipsoids show how dimensional analysisenables both deep neural networks and sparse regression to reproduce old results, e.g., Stokes law fora sphere, and generate new ones, e.g., an expression for an ellipsoid misaligned with the flow direction.Our results suggest the need to incorporate other physics-based symmetries and invariancesinto ML-based techniques for equation discovery. 

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2. The Forward Physics Facility: Sites, experiments, and physics potential

   https://doi.org/10.1016/j.physrep.2022.04.004  

   Anchordoqui, Luis A.; Ariga, Akitaka; Ariga, Tomoko; Bai, Weidong; Balazs, Kincso; Batell, Brian; Boyd, Jamie; Bramante, Joseph; Campanelli, Mario; Carmona, Adrian; et al (July 2022, Physics Reports)
3. Methods for Analyzing Physics Student Retention and Physics Curricula

   Hansen, John D (December 2023, West Virginia University)
4. Assessing physics quantitative literacy development in algebra-based physics

   https://doi.org/10.1103/lnd6-pxyt  

   Zimmerman, Charlotte; Olsho, Alexis; Smith, Trevor I; Eaton, Philip; White_Brahmia, Suzanne (July 2025, Physical Review Physics Education Research)

   Quantitative reasoning is an essential learning objective of physics instruction. The Physics Inventory for Quantitative Literacy (PIQL) is a published assessment tool that has been developed for calculus-based physics courses to help instructors evaluate whether their students learn to reason this way. However, the PIQL is not appropriate for the large population of students taking physics who are not enrolled in, or have not completed, calculus. To address this need, we have developed the General Equation-based Reasoning inventory of QuaNtity (GERQN). The GERQN is an of the PIQL and is appropriate for most physics students; the only requirement is that students have taken algebra, so they are familiar with the use of variables, negative quantities, and linear functions. In this paper, we present the development and validation of the GERQN, and a short discussion on how the GERQN can be used by instructors to help their students learn. 

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5. Assessing physics quantitative literacy in algebra-based physics: lessons learned

   https://doi.org/10.1119/perc.2022.pr.Zimmerman  

   Zimmerman, Charlotte; McCarty, Andrew; White Brahmia, Suzanne; Olsho, Alexis; De Cock, Mieke; Boudreaux, Andrew; Smith, Trevor I.; Eaton, Philip (September 2022, Assessing physics quantitative literacy in algebra-based physics: lessons learned)