Steven Atkinson

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Salvatore TorquatoLewis Bernard Professor of Natural Sciences, Department of Chemistry, Physics and Materials ScienceAdresă de e-mail confirmată pe princeton.edu
Sayan GhoshSenior Engineer at GE Aerospace ResearchAdresă de e-mail confirmată pe ge.com
Nicholas ZabarasProfessor, Cornell UniversityAdresă de e-mail confirmată pe nd.edu
Piyush PanditaStaff Engineer, GE AerospaceAdresă de e-mail confirmată pe ge.com
Valeria AndreoliAerodynamics Engineer, GE ResearchAdresă de e-mail confirmată pe ge.com
Waad SubberHoneywellAdresă de e-mail confirmată pe honeywell.com
Cheng PengUniversity of Notre Dame, Applied Mathematics and Computational StatisticsAdresă de e-mail confirmată pe nd.edu
Natarajan Chennimalai KumarGeneral Electric Global Research CenterAdresă de e-mail confirmată pe ge.com
Yiming ZhangZhejiang UniversityAdresă de e-mail confirmată pe zju.edu.cn
Ge ZhangAssistant Professor in Physics, City University of Hong KongAdresă de e-mail confirmată pe cityu.edu.hk
Govinda Anantha PadmanabhaCornell UniversityAdresă de e-mail confirmată pe cornell.edu
Yang Jiao (焦阳)Professor of Materials Science & Engineering, Dept. of Physics, Arizona State UniversityAdresă de e-mail confirmată pe asu.edu
Roger GhanemProfessor Civil Engineering, University of Southern CaliforniaAdresă de e-mail confirmată pe usc.edu
Suryarghya ChakrabartiSenior Engineer, GE AviationAdresă de e-mail confirmată pe osu.edu
Karel MatousProfessor of Aerospace and Mechanical EngineeringAdresă de e-mail confirmată pe nd.edu
Andrew GillmanAir Force Research LaboratoryAdresă de e-mail confirmată pe us.af.mil
Sandipp Krishnan RaviResearch Engineer at GE ResearchAdresă de e-mail confirmată pe umich.edu

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Steven Atkinson

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Machine Learning Uncertainty Quantification Statistical Mechanics Packing Problems


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Existence of isostatic, maximally random jammed monodisperse hard-disk packings S Atkinson, FH Stillinger, S Torquato Proceedings of the National Academy of Sciences 111 (52), 18436-18441, 2014	103	2014
Critical slowing down and hyperuniformity on approach to jamming S Atkinson, G Zhang, AB Hopkins, S Torquato Physical Review E 94 (1), 012902, 2016	87	2016
Detailed characterization of rattlers in exactly isostatic, strictly jammed sphere packings S Atkinson, FH Stillinger, S Torquato Physical Review E 88 (6), 062208, 2013	59	2013
Data-driven discovery of free-form governing differential equations S Atkinson, W Subber, L Wang, G Khan, P Hawi, R Ghanem arXiv preprint arXiv:1910.05117, 2019	55	2019
Maximally dense packings of two-dimensional convex and concave noncircular particles S Atkinson, Y Jiao, S Torquato Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 86 (3 …, 2012	55	2012
Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion S Atkinson, N Zabaras Journal of Computational Physics 383, 166-195, 2019	44	2019
Advances in bayesian probabilistic modeling for industrial applications S Ghosh, P Pandita, S Atkinson, W Subber, Y Zhang, NC Kumar, ... ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B …, 2020	42	2020
Microstructure-statistics-property relations of anisotropic polydisperse particulate composites using tomography A Gillman, K Matouš, S Atkinson Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 87 (2 …, 2013	41	2013
Inverse aerodynamic design of gas turbine blades using probabilistic machine learning S Ghosh, G Anantha Padmanabha, C Peng, V Andreoli, S Atkinson, ... Journal of Mechanical Design 144 (2), 021706, 2022	25	2022
Static structural signatures of nearly jammed disordered and ordered hard-sphere packings: Direct correlation function S Atkinson, FH Stillinger, S Torquato Physical Review E 94 (3), 032902, 2016	21	2016
Bayesian Hidden Physics Models: Uncertainty Quantification for Discovery of Nonlinear Partial Differential Operators from Data S Atkinson arXiv preprint arXiv:2006.04228, 2020	13	2020
Pro-ML IDeAS: A Probabilistic Framework for Explicit Inverse Design using Invertible Neural Network S Ghosh, GA Padmanabha, C Peng, S Atkinson, V Andreoli, P Pandita, ... AIAA Scitech 2021 Forum, 0465, 2021	12	2021
Transfer Learning Based Modeling of Industrial Turbine Airfoil Characteristics S Krishnan, S Ghosh, S Atkinson, V Andreoli, T Vandeputte, L Wang AIAA SCITECH 2022 Forum, 2105, 2022	8	2022
Structured Bayesian Gaussian process latent variable model S Atkinson, N Zabaras arXiv preprint arXiv:1805.08665, 2018	7	2018
Bayesian task embedding for few-shot Bayesian optimization S Atkinson, S Ghosh, N Chennimalai Kumar, G Khan, L Wang AIAA Scitech 2020 Forum, 1145, 2020	2	2020
AIRFRAME DIGITAL TWIN (ADT) Delivery Order FA8650-17-F-2219: Scalable, Accurate, Flexible, Efficient, Robust, Prognostic and Probabilistic Individual Aircraft Tracking (SAFER … L Wang, I Asher, S Atkinson, G Khan, R Longtin, D Ball, R Shannon, ... GENERAL ELECTRIC CO NISKAYUNA NY NISKAYUNA United States, 2019	1	2019
A Probabilistic Machine Learning Framework for Explicit Inverse Design of Industrial Gas Turbine Blades S Ghosh, V Andreoli, GA Padmanabha, C Peng, S Atkinson, P Pandita, ... Turbo Expo: Power for Land, Sea, and Air 85031, V09BT27A003, 2021		2021
Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models S Atkinson, Y Zhang, L Wang arXiv preprint arXiv:2103.07502, 2021		2021
Structure and rigidity in maximally random jammed packings of hard particles SD Atkinson Princeton University, 2016		2016
Three-Dimensional Characterization of Polydisperse Particulate Composites from Microtomography S Atkinson		2011

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