67 citations · 170 across the 7 of their papers we have counts for
10 papers
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces
Qiuyi Chen, Panagiotis Tsilifis, Mark Fuge
Solving inverse problems in scientific and engineering fields has long been intriguing and holds great potential for many applications, yet most techniques still struggle to addres…
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
Panagiotis Tsilifis, Piyush Pandita, Sayan Ghosh +3
We present a Bayesian approach to identify optimal transformations that map model input points to low dimensional latent variables. The "projection" mapping consists of an orthonor…
A Multilevel Stochastic Gradient method for PDE-constrained Optimal Control Problems with uncertain parameters
Matthieu Martin, Fabio Nobile, Panagiotis Tsilifis
In this paper, we present a multilevel Monte Carlo (MLMC) version of the Stochastic Gradient (SG) method for optimization under uncertainty, in order to tackle Optimal Control Prob…
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
Panagiotis Tsilifis, Iason Papaioannou, Daniel Straub +1
The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the computational efficiency and accuracy under a limited number of model simulations. These challenge…
Compressive sensing adaptation for polynomial chaos expansions
Panagiotis Tsilifis, Xun Huan, Cosmin Safta +5
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ. Several rotations have been proposed in the literature resulting in adapta…
Gradient-informed basis adaptation for Legendre Chaos expansions
Panagiotis A. Tsilifis
The recently introduced basis adaptation method for Homogeneous (Wiener) Chaos expansions is explored in a new context where the rotation/projection matrices are computed by discov…
Reduced Wiener Chaos representation of random fields via basis adaptation and projection
Panagiotis Tsilifis, Roger Ghanem
A new characterization of random fields appearing in physical models is presented that is based on their well-known Homogeneous Chaos expansions. We take advantage of the adaptatio…
Efficient Bayesian experimentation using an expected information gain lower bound
Panagiotis Tsilifis, Roger G. Ghanem, Paris Hajali
Experimental design is crucial for inference where limitations in the data collection procedure are present due to cost or other restrictions. Optimal experimental designs determin…
Variational Reformulation of Bayesian Inverse Problems
Panagiotis Tsilifis, Ilias Bilionis, Ioannis Katsounaros +1
The classical approach to inverse problems is based on the optimization of a misfit function. Despite its computational appeal, such an approach suffers from many shortcomings, e.g…
Cubic-Quintic Long-Range Interactions With Double Well Potentials
Panagiotis A. Tsilifis, Panagiotis G. Kevrekidis, Vassilios M. Rothos
In the present work, we examine the combined effects of cubic and quintic terms of the long range type in the dynamics of a double well potential. Employing a two-mode approximatio…