Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides.

An information field theory approach to Bayesian state and parameter estimation in dynamical systems.

A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx.

A computational framework for making early design decisions in deep space habitats.

A Bayesian Hierarchical Model for Extracting Individuals' Theory-Based Causal Knowledge.

Automated image localization to support rapid building reconnaissance in a large-scale area.

Physics-informed information field theory for modeling physical systems with uncertainty quantification.

Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields.

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

Data Driven Modeling of Turbocharger Turbine using Koopman Operator.

Physics-informed neural networks for solving parametric magnetostatic problems.

Similarity learning to enable building searches in post-event image data.

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

Exploratory Data Analysis for Airline Disruption Management.

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

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

Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks.

Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks.

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

Automated building image extraction from 360° panoramas for postdisaster evaluation.

Modeling the System Acquisition Using Deep Reinforcement Learning.

Machine learning for high-dimensional dynamic stochastic economies.

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

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

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

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

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

Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints.

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

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

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification.

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

Strategic information revelation in collaborative design.

Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation.

Uncertainty propagation using infinite mixture of Gaussian processes and variational Bayesian inference.

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

Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification.

Multi-output local Gaussian process regression: Applications to uncertainty quantification.

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

Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems.