Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems. - Phaedon-Stelios Koutsourelakis; Elias Bilionis (Multiscale Model. Simul., 2011)

Free energy computations by minimization of Kullback-Leibler divergence: An efficient adaptive biasing potential method for sparse representations. - Ilias Bilionis; Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2012)

Multi-output local Gaussian process regression: Applications to uncertainty quantification. - Ilias Bilionis; Nicholas Zabaras (J. Comput. Phys., 2012)

Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification. - Ilias Bilionis; Nicholas Zabaras (SIAM J. Sci. Comput., 2012)

Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification. - Ilias Bilionis; Nicholas Zabaras; Bledar A. Konomi; Guang Lin 001 (J. Comput. Phys., 2013)

Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference. - Peng Chen; Nicholas Zabaras; Ilias Bilionis (J. Comput. Phys., 2015)

Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation. - Rohit Tripathy 001; Ilias Bilionis; Marcial Gonzalez (J. Comput. Phys., 2016)

Strategic information revelation in collaborative design. - Adam Dachowicz; Siva Chaitanya Chaduvula; Mikhail J. Atallah; Ilias Bilionis; Jitesh H. Panchal (Adv. Eng. Informatics, 2018)

Deriving Information Acquisition Criteria For Sequentially Inferring The Expected Value Of A Black-Box Function. - Piyush Pandita; Ilias Bilionis; Jitesh H. Panchal (CoRR, 2018)

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification. - Rohit K. Tripathy 001; Ilias Bilionis (J. Comput. Phys., 2018)

Automated Detection of Pre-Disaster Building Images from Google Street View. - Chul Min Yeum; Ali Lenjani; Shirley J. Dyke; Ilias Bilionis (CoRR, 2019)

A Principal-Agent Model of Systems Engineering Processes with Application to Satellite Design. - Salar Safarkhani; Vikranth Reddy Kattakuri; Ilias Bilionis; Jitesh H. Panchal (CoRR, 2019)

Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints. - Nimish Awalgaonkar; Ilias Bilionis; Xiaoqi Liu; Panagiota Karava; Athanasios Tzempelikos (CoRR, 2019)

Towards a Theory of Systems Engineering Processes: A Principal-Agent Model of a One-Shot, Shallow Process. - Salar Safarkhani; Ilias Bilionis; Jitesh H. Panchal (CoRR, 2019)

Automated Building Image Extraction from 360-degree Panoramas for Post-Disaster Evaluation. - Ali Lenjani; Chul Min Yeum; Shirley Dyke; Ilias Bilionis (CoRR, 2019)

A Resilience-based Method for Prioritizing Post-event Building Inspections. - Ali Lenjani; Ilias Bilionis; Shirley Dyke; Chul Min Yeum; Ricardo Monteiro (CoRR, 2019)

Towards fully automated post-event data collection and analysis: pre-event and post-event information fusion. - Ali Lenjani; Shirley J. Dyke; Ilias Bilionis; Chul Min Yeum; Kenzo Kamiya; Jongseong Choi 001; Xiaoyu Liu; Arindam G. Chowdhury (CoRR, 2019)

Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design. - Piyush Pandita; Nimish Awalgaonkar; Ilias Bilionis; Jitesh H. Panchal (CoRR, 2019)

Machine learning for high-dimensional dynamic stochastic economies. - Simon Scheidegger; Ilias Bilionis (J. Comput. Sci., 2019)

Modeling the System Acquisition Using Deep Reinforcement Learning. - Salar Safarkhani; Ilias Bilionis; Jitesh H. Panchal (IEEE Access, 2020)

Automated building image extraction from 360° panoramas for postdisaster evaluation. - Ali Lenjani; Chul Min Yeum; Shirley Dyke; Ilias Bilionis (Comput. Aided Civ. Infrastructure Eng., 2020)

Improving Reconstructive Surgery Design using Gaussian Process Surrogates to Capture Material Behavior Uncertainty. - Casey Stowers; Taeksang Lee; Ilias Bilionis; Arun Gosain; Adrian Buganza Tepole (CoRR, 2020)

Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks. - Alex D. Casey; Steven F. Son; Ilias Bilionis; Brian C. Barnes (J. Chem. Inf. Model., 2020)

Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks. - Sharmila Karumuri; Rohit Tripathy 001; Ilias Bilionis; Jitesh H. Panchal (J. Comput. Phys., 2020)

Automated Indoor Image Localization to Support a Post-Event Building Assessment. - Xiaoyu Liu; Shirley J. Dyke; Chul Min Yeum; Ilias Bilionis; Ali Lenjani; Jongseong Choi 001 (Sensors, 2020)

Toward a Theory of Systems Engineering Processes: A Principal-Agent Model of a One-Shot, Shallow Process. - Salar Safarkhani; Ilias Bilionis; Jitesh H. Panchal (IEEE Syst. J., 2020)

Non-invasive Detection of Bowel Sounds in Real-life Settings Using Spectrogram Zeros and Autoencoding. - Ilias Bilionis; Georgios K. Apostolidis; Vasileios S. Charisis; Christos N. Liatsos; Leontios J. Hadjileontiadis (EMBC, 2021)

Exploratory Data Analysis for Airline Disruption Management. - Kolawole Ogunsina; Ilias Bilionis; Daniel DeLaurentis (CoRR, 2021)

Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known. - Marios Papamichalis; Abhishek Ray; Ilias Bilionis; Karthik N. Kannan; Rajiv Krishnamurthy (CoRR, 2021)

Similarity learning to enable building searches in post-event image data. - Jongseong Choi 001; Ju An Park; Shirley J. Dyke; Chul Min Yeum; Xiaoyu Liu; Ali Lenjani; Ilias Bilionis (Comput. Aided Civ. Infrastructure Eng., 2022)

Physics-informed neural networks for solving parametric magnetostatic problems. - Andrés Beltrán-Pulido; Ilias Bilionis; Dionysios Aliprantis (CoRR, 2022)

Data Driven Modeling of Turbocharger Turbine using Koopman Operator. - Shrenik Zinage; Suyash Jadhav; Yifei Zhou; Ilias Bilionis; Peter H. Meckl (CoRR, 2022)

Learning to solve Bayesian inverse problems: An amortized variational inference approach. - Sharmila Karumuri; Ilias Bilionis (CoRR, 2023)

Physics-informed information field theory for modeling physical systems with uncertainty quantification. - Alex Alberts; Ilias Bilionis (J. Comput. Phys., 2023)

Automated image localization to support rapid building reconnaissance in a large-scale area. - Xiaoyu Liu; Shirley J. Dyke; Ali Lenjani; Ilias Bilionis; Xin Zhang; Jongseong Choi 001 (Comput. Aided Civ. Infrastructure Eng., 2023)

A Bayesian Hierarchical Model for Extracting Individuals' Theory-Based Causal Knowledge. - Atharva Hans; Ashish M. Chaudhari; Ilias Bilionis; Jitesh H. Panchal (J. Comput. Inf. Sci. Eng., 2023)

A computational framework for making early design decisions in deep space habitats. - Amir Behjat; Xiaoyu Liu; Oscar Forero; Roman Ibrahimov; Shirley Dyke; Ilias Bilionis; Julio Alfonso Ramirez; Dawn Whitaker (Adv. Eng. Softw., 2024)

A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx. - Shrenik Zinage; Ilias Bilionis; Peter H. Meckl (CoRR, 2024)

An information field theory approach to Bayesian state and parameter estimation in dynamical systems. - Kairui Hao; Ilias Bilionis (J. Comput. Phys., 2024)

Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides. - Sharmila Karumuri; Ilias Bilionis (J. Comput. Phys., 2024)

Generative hyperelasticity with physics-informed probabilistic diffusion fields. - Vahidullah Tac; Manuel K. Rausch; Ilias Bilionis; Francisco Sahli Costabal; Adrian Buganza Tepole (Eng. Comput., 2025)

An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation. - Alex Alberts; Ilias Bilionis (CoRR, 2025)