Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, (373) 2052

By D. Feltham, S. Bacon, M. Brandon, J. Hunt. 2015

234 citations recorded by the source corpus.

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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…”
Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis (2017, methodology): “In this case, PCM implements the likelihood-ratio test, which by the Neyman-Pearson lemma [24] is the most powerful test.”