Ilias Bilionis

Orcid: 0000-0002-5266-105X

According to our database1, Ilias Bilionis authored at least 40 papers between 2011 and 2024.

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Bibliography

2024
Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides.
J. Comput. Phys., 2024

An information field theory approach to Bayesian state and parameter estimation in dynamical systems.
J. Comput. Phys., 2024

A computational framework for making early design decisions in deep space habitats.
Adv. Eng. Softw., 2024

2023
A Bayesian Hierarchical Model for Extracting Individuals' Theory-Based Causal Knowledge.
J. Comput. Inf. Sci. Eng., June, 2023

Automated image localization to support rapid building reconnaissance in a large-scale area.
Comput. Aided Civ. Infrastructure Eng., January, 2023

Physics-informed information field theory for modeling physical systems with uncertainty quantification.
J. Comput. Phys., 2023

Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields.
CoRR, 2023

Learning to solve Bayesian inverse problems: An amortized variational inference approach.
CoRR, 2023

2022
Data Driven Modeling of Turbocharger Turbine using Koopman Operator.
CoRR, 2022

Physics-informed neural networks for solving parametric magnetostatic problems.
CoRR, 2022

Similarity learning to enable building searches in post-event image data.
Comput. Aided Civ. Infrastructure Eng., 2022

2021
Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known.
CoRR, 2021

Exploratory Data Analysis for Airline Disruption Management.
CoRR, 2021

Non-invasive Detection of Bowel Sounds in Real-life Settings Using Spectrogram Zeros and Autoencoding.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

2020
Toward a Theory of Systems Engineering Processes: A Principal-Agent Model of a One-Shot, Shallow Process.
IEEE Syst. J., 2020

Automated Indoor Image Localization to Support a Post-Event Building Assessment.
Sensors, 2020

Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks.
J. Comput. Phys., 2020

Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks.
J. Chem. Inf. Model., 2020

Improving Reconstructive Surgery Design using Gaussian Process Surrogates to Capture Material Behavior Uncertainty.
CoRR, 2020

Automated building image extraction from 360° panoramas for postdisaster evaluation.
Comput. Aided Civ. Infrastructure Eng., 2020

Modeling the System Acquisition Using Deep Reinforcement Learning.
IEEE Access, 2020

2019
Machine learning for high-dimensional dynamic stochastic economies.
J. Comput. Sci., 2019

Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design.
CoRR, 2019

Towards fully automated post-event data collection and analysis: pre-event and post-event information fusion.
CoRR, 2019

A Resilience-based Method for Prioritizing Post-event Building Inspections.
CoRR, 2019

Automated Building Image Extraction from 360-degree Panoramas for Post-Disaster Evaluation.
CoRR, 2019

Towards a Theory of Systems Engineering Processes: A Principal-Agent Model of a One-Shot, Shallow Process.
CoRR, 2019

Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints.
CoRR, 2019

A Principal-Agent Model of Systems Engineering Processes with Application to Satellite Design.
CoRR, 2019

Automated Detection of Pre-Disaster Building Images from Google Street View.
CoRR, 2019

2018
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification.
J. Comput. Phys., 2018

Deriving Information Acquisition Criteria For Sequentially Inferring The Expected Value Of A Black-Box Function.
CoRR, 2018

Strategic information revelation in collaborative design.
Adv. Eng. Informatics, 2018

2016
Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation.
J. Comput. Phys., 2016

2015
Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference.
J. Comput. Phys., 2015

2013
Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification.
J. Comput. Phys., 2013

2012
Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification.
SIAM J. Sci. Comput., 2012

Multi-output local Gaussian process regression: Applications to uncertainty quantification.
J. Comput. Phys., 2012

Free energy computations by minimization of Kullback-Leibler divergence: An efficient adaptive biasing potential method for sparse representations.
J. Comput. Phys., 2012

2011
Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems.
Multiscale Model. Simul., 2011


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