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Panagiotis Tsilifis
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2020 – today
- 2024
- [i3]Qiuyi Chen, Panagiotis Tsilifis, Mark D. Fuge:
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces. CoRR abs/2405.14008 (2024) - 2022
- [j8]Panagiotis Tsilifis, Piyush Pandita, Sayan Ghosh, Liping Wang:
Multifidelity Model Calibration in Structural Dynamics Using Stochastic Variational Inference on Manifolds. Entropy 24(9): 1291 (2022) - 2020
- [j7]Panagiotis Tsilifis, Iason Papaioannou, Daniel Straub, Fabio Nobile:
Sparse Polynomial Chaos expansions using variational relevance vector machines. J. Comput. Phys. 416: 109498 (2020) - [i2]Panagiotis Tsilifis, Piyush Pandita, Sayan Ghosh, Valeria Andreoli, Thomas Vandeputte, Liping Wang:
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes. CoRR abs/2008.02386 (2020)
2010 – 2019
- 2019
- [j6]Panagiotis Tsilifis, Xun Huan, Cosmin Safta, Khachik Sargsyan, Guilhem Lacaze, Joseph C. Oefelein, Habib N. Najm, Roger G. Ghanem:
Compressive sensing adaptation for polynomial chaos expansions. J. Comput. Phys. 380: 29-47 (2019) - [i1]Panagiotis Tsilifis, Iason Papaioannou, Daniel Straub, Fabio Nobile:
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines. CoRR abs/1912.11029 (2019) - 2018
- [j5]Panagiotis Tsilifis, William J. Browning, Thomas E. Wood, Paul K. Newton, Roger G. Ghanem:
The Stochastic Quasi-chemical Model for Bacterial Growth: Variational Bayesian Parameter Update. J. Nonlinear Sci. 28(1): 371-393 (2018) - 2017
- [j4]Charanraj Thimmisetty, Panagiotis Tsilifis, Roger G. Ghanem:
Homogeneous chaos basis adaptation for design optimization under uncertainty: Application to the oil well placement problem. Artif. Intell. Eng. Des. Anal. Manuf. 31(3): 265-276 (2017) - [j3]Panagiotis Tsilifis, Roger G. Ghanem:
Reduced Wiener Chaos representation of random fields via basis adaptation and projection. J. Comput. Phys. 341: 102-120 (2017) - [j2]Panagiotis Tsilifis, Roger G. Ghanem, Paris Hajali:
Efficient Bayesian Experimentation Using an Expected Information Gain Lower Bound. SIAM/ASA J. Uncertain. Quantification 5(1): 30-62 (2017) - 2016
- [j1]Theodora Chaspari, Andreas Tsiartas, Panagiotis Tsilifis, Shrikanth S. Narayanan:
Markov Chain Monte Carlo Inference of Parametric Dictionaries for Sparse Bayesian Approximations. IEEE Trans. Signal Process. 64(12): 3077-3092 (2016)
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