| Compressive sensing adaptation for polynomial chaos expansions P Tsilifis, X Huan, C Safta, K Sargsyan, G Lacaze, JC Oefelein, HN Najm, ... Journal of Computational Physics 380, 29-47, 2019 | 62 | 2019 |
| Reduced Wiener Chaos representation of random fields via basis adaptation and projection P Tsilifis, RG Ghanem Journal of Computational Physics 341, 102-120, 2017 | 49 | 2017 |
| Efficient Bayesian Experimentation Using an Expected Information Gain Lower Bound P Tsilifis, RG Ghanem, P Hajali SIAM/ASA Journal on Uncertainty Quantification 5 (1), 30-62, 2017 | 44 | 2017 |
| Bayesian adaptation of chaos representations using variational inference and sampling on geodesics P Tsilifis, RG Ghanem Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2018 | 26 | 2018 |
| Homogeneous chaos basis adaptation for design optimization under uncertainty: Application to the oil well placement problem C Thimmisetty, P Tsilifis, R Ghanem Ai Edam 31 (3), 265-276, 2017 | 25 | 2017 |
| Computationally efficient variational approximations for Bayesian inverse problems P Tsilifis, I Bilionis, I Katsounaros, N Zabaras Journal of Verification, Validation and Uncertainty Quantification 1 (3), 031004, 2016 | 23 | 2016 |
| Sparse Polynomial Chaos expansions using variational relevance vector machines P Tsilifis, I Papaioannou, D Straub, F Nobile Journal of Computational Physics 416, 109498, 2020 | 21 | 2020 |
| Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes P Tsilifis, P Pandita, S Ghosh, V Andreoli, T Vandeputte, L Wang Computer Methods in Applied Mechanics and Engineering 386, 114147, 2021 | 19 | 2021 |
| A Multilevel Stochastic Gradient method for PDE-constrained Optimal Control Problems with uncertain parameters M Martin, F Nobile, P Tsilifis arXiv preprint arXiv:1912.11900, 2019 | 17 | 2019 |
| Surrogate-based sequential Bayesian experimental design using non-stationary Gaussian Processes P Pandita, P Tsilifis, NM Awalgaonkar, I Bilionis, J Panchal Computer Methods in Applied Mechanics and Engineering 385, 114007, 2021 | 12 | 2021 |
| Gradient-Informed Basis Adaptation for Legendre Chaos Expansions PA Tsilifis Journal of Verification, Validation and Uncertainty Quantification 3 (1), 011005, 2018 | 12* | 2018 |
| Markov Chain Monte Carlo Inference of Parametric Dictionaries for Sparse Bayesian Approximations T Chaspari, A Tsiartas, P Tsilifis, SS Narayanan IEEE Transactions on Signal Processing 64 (12), 3077-3092, 2016 | 10 | 2016 |
| Scalable fully bayesian gaussian process modeling and calibration with adaptive sequential monte carlo for industrial applications P Pandita, P Tsilifis, S Ghosh, L Wang Journal of Mechanical Design 143 (7), 074502, 2021 | 7 | 2021 |
| Cubic-Quintic Long-Range Interactions With Double Well Potentials PA Tsilifis, PG Kevrekidis, VM Rothos Journal of Physics A: Mathematical and Theoretical 47 (3), 035201, 2014 | 7 | 2014 |
| Inverse Design under Uncertainty using Conditional Normalizing Flows P Tsilifis, S Ghosh, V Andreoli AIAA SCITECH 2022 Forum, 0631, 2022 | 3 | 2022 |
| Dimensionality Reduction for Multi-Fidelity Gaussian Processes using Bayesian Adaptation P Tsilifis, P Pandita, S Ghosh, L Wang AIAA Scitech 2021 Forum, 1588, 2021 | 3 | 2021 |
| The stochastic quasi-chemical model for bacterial growth: Variational bayesian parameter update P Tsilifis, WJ Browning, TE Wood, PK Newton, RG Ghanem Journal of Nonlinear Science 28, 371-393, 2018 | 3 | 2018 |
| Variational Reformulation of Bayesian Inverse Problems P Tsilifis, I Bilionis, I Katsounaros, N Zabaras UNCECOMP 2015, 1st ECCOMAS Thematic Conference on Uncertainty Quantification …, 2015 | 2 | 2015 |
| Multifidelity Model Calibration in Structural Dynamics Using Stochastic Variational Inference on Manifolds P Tsilifis, P Pandita, S Ghosh, L Wang Entropy 24 (9), 1291, 2022 | 1 | 2022 |
| High Fidelity Digital Twin for Critical System Assessments YJ Wang, K Shirvan, E Baglietto, P Tsilifis, A Amer, G Khan, L Wang 19th Int. Top. Meet. Nucl. React. Therm. Hydraul. NURETH-19, Brussels, Belgium, 2022 | 1 | 2022 |
Roger GhanemProfessor Civil Engineering, University of Southern CaliforniaVerified email at usc.edu
