Physics Education
- [1] arXiv:2405.14761 [pdf, ps, html, other]
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Title: Effective & Ethical Mentorship in Physics and Astronomy through Grassroots OrganizationsComments: 30 pages single spaced with appendix of figures, 8 figures, published in BAASJournal-ref: Bulletin of the AAS, 56(1) (2024)Subjects: Physics Education (physics.ed-ph); Cosmology and Nongalactic Astrophysics (astro-ph.CO); Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Physics and Society (physics.soc-ph)
Effective and ethical mentorship practices are crucial to improving recruitment and retention especially for historically minoritized groups (HMGs). Spectrum is a diversity, inclusion, equity, and accessibility (DEIA) grassroots organization committed to empowering equitable excellence through sustainable change. By improving transparency and DEIA within the fields of physics and astronomy, we can empower the next generation of diverse scientists and increase field retention. Starting within our home department at George Mason University and moving outwards, we ensure our students leave as advocates for DEIA and AJEDI (access, justice, equity, diversity, and inclusion) through education and mentorship. Spectrum is providing professionally trained peer mentors to aid students in all facets of their academic and personal lives. Although the peer mentoring program existed since the creation of Spectrum in Spring 2020, we have recently developed and implemented a formal mentorship training for both student and faculty mentors thus increasing the quality, trustworthiness, and confidence of our mentors. Using the latest mentorship research available, this training is developed by Spectrum for George Mason University, with the ability to implement the training at any institution.
New submissions for Friday, 24 May 2024 (showing 1 of 1 entries )
- [2] arXiv:2312.01891 (replaced) [pdf, ps, html, other]
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Title: A Christmas Story about Quantum TeleportationComments: 11 + 8 pages, 3 figures, v2: as publishedJournal-ref: Phys. Educ. 59, 035021 (2024)Subjects: Physics Education (physics.ed-ph); Popular Physics (physics.pop-ph); Quantum Physics (quant-ph)
Quantum teleportation is a concept that fascinates and confuses many people, in particular, given that it combines quantum physics and the concept of teleportation. With quantum teleportation likely to play a key role in several communication technologies and the quantum internet in the future, it is imperative to create learning tools and approaches that can accurately and effectively communicate the concept. Recent research has indicated the importance of teachers enthusing students about the topic of quantum physics. Therefore, educators at both high school and early university level need to find engaging and perhaps unorthodox ways of teaching complex, yet interesting topics such as quantum teleportation. In this paper, we present a paradigm to teach the concept of quantum teleportation using the Christmas gift-bringer Santa Claus. Using the example of Santa Claus, we use an unusual context to explore the key aspects of quantum teleportation, and all without being overly abstract. In addition, we outline a worksheet designed for use in the classroom setting which is based on common naive conceptions from quantum physics. This worksheet will be evaluated as a classroom resource to teach quantum teleportation in a subsequent study.
- [3] arXiv:2405.11542 (replaced) [pdf, ps, html, other]
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Title: From Fourier to Neural ODEs: Flow Matching for Modeling Complex SystemsSubjects: Machine Learning (cs.LG); Physics Education (physics.ed-ph)
Modeling complex systems using standard neural ordinary differential equations (NODEs) often faces some essential challenges, including high computational costs and susceptibility to local optima. To address these challenges, we propose a simulation-free framework, called Fourier NODEs (FNODEs), that effectively trains NODEs by directly matching the target vector field based on Fourier analysis. Specifically, we employ the Fourier analysis to estimate temporal and potential high-order spatial gradients from noisy observational data. We then incorporate the estimated spatial gradients as additional inputs to a neural network. Furthermore, we utilize the estimated temporal gradient as the optimization objective for the output of the neural network. Later, the trained neural network generates more data points through an ODE solver without participating in the computational graph, facilitating more accurate estimations of gradients based on Fourier analysis. These two steps form a positive feedback loop, enabling accurate dynamics modeling in our framework. Consequently, our approach outperforms state-of-the-art methods in terms of training time, dynamics prediction, and robustness. Finally, we demonstrate the superior performance of our framework using a number of representative complex systems.