| 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 | 477 | 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 | 202 | 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 | 198 | 2013 |
| Multi-output local Gaussian process regression: Applications to uncertainty quantification I Bilionis, N Zabaras Journal of Computational Physics 231 (17), 5718-5746, 2012 | 188 | 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 | 155 | 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 | 121 | 2017 |
| 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 | 78 | 2019 |
| Solution of inverse problems with limited forward solver evaluations: a Bayesian perspective I Bilionis, N Zabaras Inverse Problems 30 (1), 015004, 2013 | 75 | 2013 |
| Machine learning for high-dimensional dynamic stochastic economies S Scheidegger, I Bilionis Journal of Computational Science 33, 68-82, 2019 | 74 | 2019 |
| 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 | 64 | 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 | 59 | 2015 |
| 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 |
| 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 | 55 | 2018 |
| 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 | 53 | 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 | 49 | 2019 |
| 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 | 47 | 2020 |
| 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 | 46 | 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 | 45 | 2017 |
| Bayesian uncertainty propagation using Gaussian processes I Bilionis, N Zabaras Handbook of uncertainty quantification, 1-45, 2016 | 44 | 2016 |
Panagiota KaravaProfessor of Civil Engineering, Purdue University在 purdue.edu 的电子邮件经过验证
