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Resource Letter CP-3: Computational physics

American Journal of Physics, 2023
This Resource Letter provides information and guidance for those looking to incorporate computation into their courses or to refine their own computational practice. We begin with general resources, including policy documents and supportive organizations.
T. Atherton
semanticscholar   +1 more source

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging

IEEE Signal Processing Magazine, 2022
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits.
K. Hammernik, Thomas Kuestner, Burhaneddin Yaman, Zhengnan Huang, D. Rueckert, F. Knoll, M. Akçakaya   +6 more
semanticscholar   +1 more source

Racial hierarchy and masculine space: Participatory in/equity in computational physics classrooms

Computer Science Education, 2020
Background and Context Computing is being integrated into a range of STEM disciplines. Still, computing remains inaccessible to many minoritized groups, especially girls and certain people of color. In this mixed methods study, we investigated racial and
Niral Shah, J. Christensen, N. Ortiz, Ai-Khanh Nguyen, Sung-Kook Byun, David Stroupe, D. Reinholz   +6 more
semanticscholar   +1 more source

Application of Wavelet Methods in Computational Physics

Annals of Physics
The quantitative study of many physical problems ultimately boils down to solving various partial differential equations (PDEs). Wavelet analysis, known as the “mathematical microscope”, has been hailed for its excellent Multiresolution Analysis (MRA ...
Jizeng Wang, Xiaojing Liu, Y. Zhou
semanticscholar   +1 more source

Machine learning for modelling unstructured grid data in computational physics: a review

Information Fusion
Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques.
Sibo Cheng, M. Bocquet, Weiping Ding, T. S. Finn, Rui Fu, Jinlong Fu, Yike Guo, Eleda Johnson, Siyi Li, Che Liu, Eric Newton Moro, Jie Pan, M. Piggott, César A. Quilodrán Casas, Prakhar Sharma, Kun Wang, Dunhui Xiao, Xiao Xue, Yong Zeng, Mingrui Zhang, Hao Zhou, Kewei Zhu, Rossella Arcucci   +22 more
semanticscholar   +1 more source

A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics

Computational Mechanics, 2022
Jinshuai Bai, T. Rabczuk, Ashish Gupta, Laith Alzubaidi, Yuantong T. Gu   +4 more
semanticscholar   +1 more source

A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics

The Physics of Fluids
Physics-informed neural networks (PINNs) represent an emerging computational paradigm that incorporates observed data patterns and the fundamental physical laws of a given problem domain.
Chi Zhao, Feifei Zhang, Wenqiang Lou, Xi Wang, Jianyong Yang   +4 more
semanticscholar   +1 more source

The imperative of physics-based modeling and inverse theory in computational science

Nature Computational Science, 2021
K. Willcox, O. Ghattas, P. Heimbach
semanticscholar   +1 more source

Emerging exciton physics in transition metal dichalcogenide heterobilayers

Nature Reviews Materials, 2022
Emma C Regan, Yongxin Zeng, Long Zhang
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