Conformalized Physics-Informed Neural Networks

Inductive confidence machines for regression

Harris Papadopoulos, Kostas Proedrou, Volodya Vovk, and Alex Gammerman · 2002

Earlier work this paper cites.

Algorithmic learning in a random world , volume 29

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Earlier work this paper cites.

Mathematical modeling of ovarian cancer treatments: sequencing of surgery and chemotherapy

M Kohandel, S Sivaloganathan, and A Oza · 2006

Earlier work this paper cites.

Adam: A method for stochastic optimization

Original

Diederik P Kingma and Jimmy Ba · 2014

Earlier work this paper cites.

Pytorch: An imperative style, high-performance deep learning library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019

Earlier work this paper cites.

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Cited alongside, same era.

Cellular interactions constrain tumor growth

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Cited alongside, same era.

A gentle introduction to conformal prediction and distribution-free uncertainty quantification

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Conformal bayesian computation

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Cited alongside, same era.

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Later among the works it cites.

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Later among the works it cites.

B-pinns: Bayesian physics-informed neural networks for forward and inverse pde problems with noisy data

Original

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Later among the works it cites.

Universal physics-informed neural networks: symbolic differential operator discovery with sparse data

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