Ilias Bilionis | Predictive Science Laboratory

Latest

Bayesian model calibration and optimization of surfactant-polymer flooding
Bayesian optimal design of experiments for inferring the statistical expectation of expensive black-box functions
Global sensitivity analysis for the design of nonlinear identification experiments
Implementation of a self-tuned HVAC controller to satisfy occupant thermal preferences and optimize energy use
A personalized daylighting control approach to dynamically optimize visual satisfaction and lighting energy use
Machine learning for high-dimensional dynamic stochastic economies
Predicting the effect of aging and defect size on the stress profiles of skin from advancement, rotation and transposition flap surgeries
Inference of thermal preference profiles for personalized thermal environments with actual building occupants
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
Propagation of material behavior uncertainty in a nonlinear finite element model of reconstructive surgery
Model predictive control under forecast uncertainty for optimal operation of buildings with integrated solar systems
Quantifying the impact of domain knowledge and problem framing on sequential decisions in engineering design
Inferring personalized visual satisfaction profiles in daylit offices from comparative preferences using a Bayesian approach
Bayesian classification and inference of occupant visual preferences in daylit perimeter private offices
Strategic information revelation in collaborative design
Stochastic multiobjective optimization on a budget: Application to multipass wire drawing with quantified uncertainties
Process optimization of graphene growth in a roll-to-roll plasma CVD system
A Bayesian approach for probabilistic classification and inference of occupant thermal preferences in office buildings
A Bayesian modeling approach of human interactions with shading and electric lighting systems in private offices
Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions
Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation
Computationally efficient variational approximations for Bayesian inverse problems
Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference
Data-driven model for solar irradiation based on satellite observations
Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification
Relative entropy as model selection tool in cluster expansions
A stochastic optimization approach to coarse-graining using a relative-entropy framework
Multi-output local Gaussian process regression: Applications to uncertainty quantification