Philosophical Transactions of the Royal Society A : Mathematical

By Stephen M. Barnett, Mohamed Babiker, Miles J. Padgett. 2017

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Machine Learning in Aerodynamic Shape Optimization (2022, methodology): “Principal Component Analysis PCA is a mathematical algorithm that transforms high-dimensional data to low dimensions while maintaining the maximum variation in the data set [198, 199].”
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks (2021, background): “While the tremendous increase in observational data, scientific and computing advancements has resulted in an increase in the forecast skill by about one day every decade [4], the recent success of deep learning in scientific domains has spurred an interest in its application to weather forecasting ”
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future (2020, methodology): “The concepts of information theory [91] and wavelet scattering, for example, have been used towards this end [92].”
Focus on environmental risks and migration: causes and consequences (2015, background): “It suggests that without significant mitigation efforts, the world is locked into high levels of impacts in all social and ecological arenas associated with 4 °Cormoremeanwarming, unevenly distributed in speed andmagnitude (Betts et al 2011).”
Geologic Constraints on Early Mars Climate (2018, background): “Gappy and spotty surface habitability would be a challenge for the persistence of surface life (Cockell 2014, Westall et al. 2015)6.”
Giant and Tunneling Magnetoresistance in Unconventional Collinear Antiferromagnets with Nonrelativistic Spin-Momentum Coupling (2021, background): “Similarly delicate were the proposed GMR and TMR effects in these antiferromagnetic structures [11] which might”
Time Series Data Augmentation for Deep Learning: A Survey (2020, methodology): “Gaussian Processes (GPs) [Rasmussen and Williams, 2005] are well-known Bayesian non-parametric models suitable for time series analysis [Roberts et al., 2013].”
Dynamical decoupling for superconducting qubits: A performance survey (2022, methodology): “The KDD sequence is robust to flip-angle errors [Eq.”
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next (2022, background): “Finally, more recent advancements by Kondor and Trivedi (2018), and Mallat (2016), brought to Raissi et al (2019) solution that extended previous notions while also introducing fundamentally new approaches, such as a discrete time-stepping scheme that efficiently leverages the predictive ability of…”
Transparent to whom? No algorithmic accountability without a critical audience (2018, background): “Such algorithms are perceived to have considerable potential toward solving some of our society ’ s most pressing issues such as mass migration and climate change (Floridi & Taddeo, 2016).”