Ilias Bilionis

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	Toate	Din 2020
Referințe bibliografice	3698	2958
h-index	31	27
i10-index	65	61

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Panagiota KaravaProfessor of Civil Engineering, Purdue UniversityAdresă de e-mail confirmată pe purdue.edu
Jitesh H. PanchalSchool of Mechanical Engineering, Purdue University, West Lafayette, INAdresă de e-mail confirmată pe purdue.edu
Nicholas ZabarasProfessor, Cornell UniversityAdresă de e-mail confirmată pe nd.edu
Athanasios TzempelikosProfessor of Civil Engineering, Purdue UniversityAdresă de e-mail confirmată pe purdue.edu
Shirley J. DykeCoates Professor of Mechanical Eng, Professor of Civil and Construction Eng, Purdue UniversityAdresă de e-mail confirmată pe purdue.edu
Seungjae LeeUniversity of TorontoAdresă de e-mail confirmată pe utoronto.ca
Rohit TripathyThe Jackson LaboratoryAdresă de e-mail confirmată pe jax.org
Piyush PanditaStaff Engineer, GE AerospaceAdresă de e-mail confirmată pe ge.com
Nimish AwalgaonkarManager, AI Research & InnovationAdresă de e-mail confirmată pe zs.com
Ali LenjaniStanford UniversityAdresă de e-mail confirmată pe stanford.edu
Chul Min YeumUniversity of WaterlooAdresă de e-mail confirmată pe uwaterloo.ca
Adrián Buganza TepolePurdue UniversityAdresă de e-mail confirmată pe purdue.edu
Sharmila KarumuriPhD, Purdue UniversityAdresă de e-mail confirmată pe purdue.edu
Atharva HansPurdue UniversityAdresă de e-mail confirmată pe purdue.edu
Seyed Amir SadeghiPurdue UniversityAdresă de e-mail confirmată pe purdue.edu
Jie XiongNRELAdresă de e-mail confirmată pe nrel.gov
Ashish M. ChaudhariData Scientist, Philips Research North AmericaAdresă de e-mail confirmată pe mit.edu
Xiaoyu LiuPostdoctoral Researcher, Purdue UniversityAdresă de e-mail confirmată pe purdue.edu
Taeksang LeeAssistant Professor of Mechanical Engineering, Myongji UniversityAdresă de e-mail confirmată pe mju.ac.kr
Marcial GonzalezSchool of Mechanical Engineering, Ray W. Herrick Laboratories, Purdue UniversityAdresă de e-mail confirmată pe purdue.edu

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Ilias Bilionis

Professor, Mechanical Engineering, Purdue University

Adresă de e-mail confirmată pe purdue.edu - Pagina de pornire

Scientific Machine Learning Uncertainty Quantification


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification RK Tripathy, I Bilionis Journal of computational physics 375, 565-588, 2018	535	2018
Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation R Tripathy, I Bilionis, M Gonzalez Journal of Computational Physics 321, 191-223, 2016	229	2016
Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification I Bilionis, N Zabaras, BA Konomi, G Lin Journal of Computational Physics 241, 212-239, 2013	208	2013
Multi-output local Gaussian process regression: Applications to uncertainty quantification I Bilionis, N Zabaras Journal of Computational Physics 231 (17), 5718-5746, 2012	206	2012
Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks S Karumuri, R Tripathy, I Bilionis, J Panchal Journal of Computational Physics 404, 109120, 2020	181	2020
A Bayesian approach for probabilistic classification and inference of occupant thermal preferences in office buildings S Lee, I Bilionis, P Karava, A Tzempelikos Building and Environment 118, 323-343, 2017	137	2017
Machine learning for high-dimensional dynamic stochastic economies S Scheidegger, I Bilionis Journal of Computational Science 33, 68-82, 2019	85	2019
Inference of thermal preference profiles for personalized thermal environments with actual building occupants S Lee, P Karava, A Tzempelikos, I Bilionis Building and Environment 148, 714-729, 2019	84	2019
Solution of inverse problems with limited forward solver evaluations: a Bayesian perspective I Bilionis, N Zabaras Inverse Problems 30 (1), 015004, 2013	78	2013
Implementation of a self-tuned HVAC controller to satisfy occupant thermal preferences and optimize energy use S Lee, J Joe, P Karava, I Bilionis, A Tzempelikos Energy and Buildings 194, 301-316, 2019	74	2019
Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference P Chen, N Zabaras, I Bilionis Journal of computational physics 284, 291-333, 2015	63	2015
Prediction of energetic material properties from electronic structure using 3D convolutional neural networks AD Casey, SF Son, I Bilionis, BC Barnes Journal of Chemical Information and Modeling 60 (10), 4457-4473, 2020	62	2020
Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression T Lee, I Bilionis, AB Tepole Computer methods in applied mechanics and engineering 359, 112724, 2020	61	2020
A personalized daylighting control approach to dynamically optimize visual satisfaction and lighting energy use J Xiong, A Tzempelikos, I Bilionis, P Karava Energy and Buildings 193, 111-126, 2019	60	2019
Quantifying the impact of domain knowledge and problem framing on sequential decisions in engineering design M Shergadwala, I Bilionis, KN Kannan, JH Panchal Journal of Mechanical Design 140 (10), 101402, 2018	60	2018
A stochastic optimization approach to coarse-graining using a relative-entropy framework I Bilionis, N Zabaras The Journal of chemical physics 138 (4), 2013	59	2013
Free energy computations by minimization of Kullback–Leibler divergence: An efficient adaptive biasing potential method for sparse representations I Bilionis, PS Koutsourelakis Journal of Computational Physics 231 (9), 3849-3870, 2012	58	2012
Automated building image extraction from 360 panoramas for postdisaster evaluation A Lenjani, CM Yeum, S Dyke, I Bilionis Computer‐Aided Civil and Infrastructure Engineering 35 (3), 241-257, 2020	52	2020
Process optimization of graphene growth in a roll-to-roll plasma CVD system MA Alrefae, A Kumar, P Pandita, A Candadai, I Bilionis, TS Fisher Aip Advances 7 (11), 2017	49	2017
Physics-informed neural networks for solving parametric magnetostatic problems A Beltrán-Pulido, I Bilionis, D Aliprantis IEEE Transactions on Energy Conversion 37 (4), 2678-2689, 2022	47	2022

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