485 citations · 878 across the 26 of their papers we have counts for
27 papers
An information field theory approach to Bayesian state and parameter estimation in dynamical systems
Kairui Hao, Ilias Bilionis
Dynamical system state estimation and parameter calibration problems are ubiquitous across science and engineering. Bayesian approaches to the problem are the gold standard as they…
Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides
Sharmila Karumuri, Ilias Bilionis
Inverse problems, i.e., estimating parameters of physical models from experimental data, are ubiquitous in science and engineering. The Bayesian formulation is the gold standard be…
Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification
Alex Alberts, Ilias Bilionis
Data-driven approaches coupled with physical knowledge are powerful techniques to model systems. The goal of such models is to efficiently solve for the underlying field by combini…
Data Driven Modeling of Turbocharger Turbine using Koopman Operator
Shrenik Zinage, Suyash Jadhav, Yifei Zhou +2
A turbocharger plays an essential part in reducing emissions and increasing the fuel efficiency of road vehicles. The pulsating flow of exhaust gases, along with high heat exchange…
Bayesian Inference of Fiber Orientation and Polymer Properties in Short Fiber-Reinforced Polymer Composites
Akshay J. Thomas, Eduardo Barocio, Ilias Bilionis +1
We present a Bayesian methodology to infer the elastic modulus of the constituent polymer and the fiber orientation state in a short-fiber reinforced polymer composite (SFRP). The…
Physics-informed neural networks for solving parametric magnetostatic problems
Andrés Beltrán-Pulido, Ilias Bilionis, Dionysios Aliprantis
The objective of this paper is to investigate the ability of physics-informed neural networks to learn the magnetic field response as a function of design parameters in the context…
Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known
Marios Papamichalis, Abhishek Ray, Ilias Bilionis +2
Probabilistic machine learning models are often insufficient to help with decisions on interventions because those models find correlations - not causal relationships. If observati…
Exploratory Data Analysis for Airline Disruption Management
Kolawole Ogunsina, Ilias Bilionis, Daniel DeLaurentis
Reliable platforms for data collation during airline schedule operations have significantly increased the quality and quantity of available information for effectively managing air…
Improving Reconstructive Surgery Design using Gaussian Process Surrogates to Capture Material Behavior Uncertainty
Casey Stowers, Taeksang Lee, Ilias Bilionis +2
Excessive loads near wounds produce pathological scarring and other complications. Presently, stress cannot easily be measured by surgeons in the operating room. Instead, surgeons…
Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design
Piyush Pandita, Nimish Awalgaonkar, Ilias Bilionis +1
Estimating arbitrary quantities of interest (QoIs) that are non-linear operators of complex, expensive-to-evaluate, black-box functions is a challenging problem due to missing doma…
A Resilience-based Method for Prioritizing Post-event Building Inspections
Ali Lenjani, Ilias Bilionis, Shirley Dyke +2
Despite the wide range of possible scenarios in the aftermath of a disruptive event, each community can make choices to improve its resilience, or its ability to bounce back. A res…
Towards fully automated post-event data collection and analysis: pre-event and post-event information fusion
Ali Lenjani, Shirley J. Dyke, Ilias Bilionis +5
In post-event reconnaissance missions, engineers and researchers collect perishable information about damaged buildings in the affected geographical region to learn from the conseq…
Automated building image extraction from 360° panoramas for postdisaster evaluation
Ali Lenjani, Chul Min Yeum, Shirley Dyke +1
After a disaster, teams of structural engineers collect vast amounts of images from damaged buildings to obtain new knowledge and extract lessons from the event. However, in many c…
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints
Nimish Awalgaonkar, Ilias Bilionis, Xiaoqi Liu +2
Thermal preferences vary from person to person and may change over time. The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optim…
A Principal-Agent Model of Systems Engineering Processes with Application to Satellite Design
Salar Safarkhani, Vikranth Reddy Kattakuri, Ilias Bilionis +1
We present a principal-agent model of a one-shot, shallow, systems engineering process. The process is one-shot in the sense that decisions are made during one time step and that t…
Towards a Theory of Systems Engineering Processes: A Principal-Agent Model of a One-Shot, Shallow Process
Salar Safarkhani, Ilias Bilionis, Jitesh Panchal
Systems engineering processes coordinate the effort of different individuals to generate a product satisfying certain requirements. As the involved engineers are self-interested ag…
Deep active subspaces - a scalable method for high-dimensional uncertainty propagation
Rohit Tripathy, Ilias Bilionis
A problem of considerable importance within the field of uncertainty quantification (UQ) is the development of efficient methods for the construction of accurate surrogate models.…
Automated Detection of Pre-Disaster Building Images from Google Street View
Chul Min Yeum, Ali Lenjani, Shirley J. Dyke +1
After a disaster, teams of structural engineers collect vast amounts of images from damaged buildings to obtain lessons and gain knowledge from the event. Images of damaged buildin…
Simulator-free Solution of High-Dimensional Stochastic Elliptic Partial Differential Equations using Deep Neural Networks
Sharmila Karumuri, Rohit Tripathy, Ilias Bilionis +1
Stochastic partial differential equations (SPDEs) are ubiquitous in engineering and computational sciences. The stochasticity arises as a consequence of uncertainty in input parame…
Bayesian Optimal Design of Experiments For Inferring The Statistical Expectation Of A Black-Box Function
Piyush Pandita, Ilias Bilionis, Jitesh Panchal
Bayesian optimal design of experiments (BODE) has been successful in acquiring information about a quantity of interest (QoI) which depends on a black-box function. BODE is charact…
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
Rohit Tripathy, Ilias Bilionis
State-of-the-art computer codes for simulating real physical systems are often characterized by a vast number of input parameters. Performing uncertainty quantification (UQ) tasks…
Stochastic Multi-objective Optimization on a Budget: Application to multi-pass wire drawing with quantified uncertainties
Piyush Pandita, Ilias Bilionis, Jitesh Panchal +3
Design optimization of engineering systems with multiple competing objectives is a painstakingly tedious process especially when the objective functions are expensive-to-evaluate c…
Probabilistic solvers for partial differential equations
Ilias Bilionis
This work is concerned with the quantification of the epistemic uncertainties induced the discretization of partial differential equations. Following the paradigm of probabilistic…
Extending Expected Improvement for High-dimensional Stochastic Optimization of Expensive Black-Box Functions
Piyush Pandita, Ilias Bilionis, Jitesh Panchal
Design optimization under uncertainty is notoriously difficult when the objective function is expensive to evaluate. State-of-the-art techniques, e.g, stochastic optimization or sa…