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We propose the novel use of a generative adversarial network (GAN) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN).
A simple automatic derivative evaluation program
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The estimation of the basic reproduction number for infectious diseases
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The mathematics of infectious diseases
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Inverse problem theory and methods for model parameter estimation
Albert Tarantola · 2005
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A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh · 2006
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Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations
G Rozza, D B P Huynh, and A T Patera · 2008
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Inverse theory for petroleum reservoir characterization and history matching
Dean S Oliver, Albert C Reynolds, and Ning Liu · 2008
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The elements of statistical learning: data mining, inference, and prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Development and application of reduced-order modeling procedures for subsurface flow simulation
Marco A Cardoso, Louis J Durlofsky, and Pallav Sarma · 2009
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Recent progress on reservoir history matching: a review
Dean S. Oliver and Yan Chen · 2011
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Auto-encoding Variational Bayes
Diederik P Kingma and Max Welling · 2013
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Nonintrusive reduced-order modeling of parametrized time-dependent partial differential equations
Christophe Audouze, Florian De Vuyst, and Prasanth B. Nair · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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A scalable approach for variational data assimilation
Luisa D’Amore, Rossella Arcucci, Luisa Carracciuolo, and Almerico Murli · 2014
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A survey of projection-based model reduction methods for parametric dynamical systems
Peter Benner, Serkan Gugercin, and Karen Willcox · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Deep Learning , volume 1
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
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A survey of deep neural network architectures and their applications
Weibo Liu, Zidong Wang, Xiaohui Liu, Nianyin Zeng, Yurong Liu, and Fuad E. Alsaadi · 2017
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History matching and production optimization under uncertainties–application of closed-loop reservoir management
Vinícius Luiz Santos Silva, Alexandre Anozé Emerick, Paulo Couto, and José Luis Drummond Alves · 2017
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Data driven approximation of parametrized PDEs by reduced basis and neural networks
Niccolò Dal Santo, Simone Deparis, and Luca Pegolotti · 2020
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Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics
Jiayang Xu and Karthik Duraisamy · 2020
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Modeling infectious disease dynamics
Sarah Cobey · 2020
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The reproductive number of COVID-19 is higher compared to SARS coronavirus
Ying Liu, Albert A Gayle, Annelies Wilder-Smith, and Joacim Rocklöv · 2020
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Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries
Seth Flaxman, Swapnil Mishra, Axel Gandy, H Unwin, Helen Coupland, T Mellan, Harisson Zhu, Tresnia Berah, J Eaton, P Perez Guzman, et al · 2020
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D. Xiao, F. Fang, C. C. Pain, and I. M. Navon · 2017
Cited alongside, same era.
Automatic differentiation in machine learning: a survey
Atılım Günes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2017
Cited alongside, same era.
Social GAN: Socially acceptable trajectories with generative adversarial networks
Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi · 2018
Cited alongside, same era.
tempoGAN: A temporally coherent, volumetric GAN for super-resolution fluid flow
You Xie, Erik Franz, Mengyu Chu, and Nils Thuerey · 2018
Cited alongside, same era.
Non-intrusive reduced order modeling of nonlinear problems using neural networks
Jan S Hesthaven and Stefano Ubbiali · 2018
Cited alongside, same era.
Epidemics: Models and data using R
Ottar N Bjørnstad · 2018
Cited alongside, same era.
Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems
Aurélien Géron · 2019
Cited alongside, same era.
Predicting CO 2 Plume Migration in Heterogeneous Formations using Conditional Deep Convolutional Generative Adversarial Network
Zhi Zhong, Alexander Y Sun, and Hoonyoung Jeong · 2019
Cited alongside, same era.
Transmission potential and severity of COVID-19 in South Korea
Eunha Shim, Amna Tariq, Wongyeong Choi, Yiseul Lee, and Gerardo Chowell · 2020
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Collocation of Next-Generation Operators for Computing the Basic Reproduction Number of Structured Populations
D Breda, T Kuniya, J Ripoll, and R Vermiglio · 2020
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Management strategies in a SEIR-type model of COVID 19 community spread
Anca Rădulescu, Cassandra Williams, and Kieran Cavanagh · 2020
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Super-spreading events initiated the exponential growth phase of COVID-19 with ℛ 0 \mathcal{R}_{0} higher than initially estimated
Marek Kochańczyk, Frederic Grabowski, and Tomasz Lipniacki · 2020
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Recent developments combining ensemble smoother and deep generative networks for facies history matching
Smith WA Canchumuni, Jose DB Castro, Júlia Potratz, Alexandre A Emerick, and Marco Aurelio C Pacheco · 2021
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Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic
César Quilodrán-Casas, Vinicius Santos Silva, Rossella Arcucci, Claire E Heaney, Yike Guo, and Christopher C Pain · 2021
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Simulating the spread of COVID-19 via a spatially-resolved susceptible–exposed–infected–recovered–deceased (SEIRD) model with heterogeneous diffusion
Alex Viguerie, Guillermo Lorenzo, Ferdinando Auricchio, Davide Baroli, Thomas JR Hughes, Alessia Patton, Alessandro Reali, Thomas E Yankeelov, and Alessandro Veneziani · 2021
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An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion
Toby R F Phillips, Claire E Heaney, Paul N Smith, and Christopher C Pain · 2021
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Non-Intrusive Reduced-Order Modeling of Parameterized Electromagnetic Scattering Problems using Cubic Spline Interpolation
Kun Li, Ting-Zhu Huang, Liang Li, and Stéphane Lanteri · 2021
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A fully adaptive nonintrusive reduced-order modelling approach for parametrized time-dependent problems
Fahad Alsayyari, Zoltán Perkó, Marco Tiberga, Jan Leen Kloosterman, and Danny Lathouwers · 2021
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Parameterized nonintrusive reduced-order model for general unsteady flow problems using artificial neural networks
Oliviu Şugar Gabor · 2021
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Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
Romit Maulik, Bethany Lusch, and Prasanna Balaprakash · 2021
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A Comprehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs
S Fresca, L Dede, and A Manzoni · 2021
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Stefanos Nikolopoulos, Ioannis Kalogeris, and Vissarion Papadopoulos · 2021
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Bifidelity Data-Assisted Neural Networks in Nonintrusive Reduced-Order Modeling
C Lu and X Zhu · 2021
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