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Found 19 papers, 7 papers with code
Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields
We extend our approach to spatially correlated diffusion resulting in heterogeneous material properties for arbitrary geometries.
An information field theory approach to Bayesian state and parameter estimation in dynamical systems
To address these drawbacks, the objective of this paper is to develop a scalable Bayesian approach to state and parameter estimation suitable for continuous-time, deterministic dynamical systems.
Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides
We demonstrate our approach by solving a set of benchmark problems from science and engineering.
Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification
As an addendum, the method is equipped with a metric which allows the posterior to automatically quantify model-form uncertainty.
Data Driven Modeling of Turbocharger Turbine using Koopman Operator
The objective of this paper is to develop a model to predict the transient and steady-state behavior of the turbine using the Koopman operator which can be helpful for control design and analysis.
Physics-informed neural networks for solving parametric magnetostatic problems
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 of a two-dimensional (2-D) magnetostatic problem.
Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known
Probabilistic machine learning models are often insufficient to help with decisions on interventions because those models find correlations - not causal relationships.
Exploratory Data Analysis for Airline Disruption Management
Reliable platforms for data collation during airline schedule operations have significantly increased the quality and quantity of available information for effectively managing airline schedule disruptions.
Improving Reconstructive Surgery Design using Gaussian Process Surrogates to Capture Material Behavior Uncertainty
The optimization task relies on the efficiency of the GP surrogates to calculate the expected cost of different strategies when the uncertainty of other material parameters is included.
Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design
We model the underlying information source as a fully-Bayesian, non-stationary Gaussian process (FBNSGP), and derive an approximation of the information gain of a hypothetical experiment about an arbitrary QoI conditional on the hyper-parameters The EIG about the same QoI is estimated by sample averages to integrate over the posterior of the hyper-parameters and the potential experimental outcomes.
Towards fully automated post-event data collection and analysis: pre-event and post-event information fusion
A typical post-event reconnaissance mission is conducted by first doing a preliminary survey, followed by a detailed survey.
Automated building image extraction from 360° panoramas for postdisaster evaluation
Here, to address this issue, we develop a method to automatically extract pre-event building images from 360o panorama images (panoramas).
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints
The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optimally learn the indoor air temperature values which maximize their satisfaction.
Deep active subspaces - a scalable method for high-dimensional uncertainty propagation
The difficulty of accurate surrogate modeling in such systems, is further compounded by data scarcity brought about by the large cost of forward model evaluations.
Simulator-free Solution of High-Dimensional Stochastic Elliptic Partial Differential Equations using Deep Neural Networks
We propose a novel methodology for high-dimensional uncertainty propagation of elliptic SPDEs which lifts the requirement for a deterministic forward solver.
Data Analysis, Statistics and Probability Computational Physics
Automated Detection of Pre-Disaster Building Images from Google Street View
Since the panoramas are collected from various locations near the building along the street, the user can identify its pre-disaster conditions from the full set of external views.
Bayesian Optimal Design of Experiments For Inferring The Statistical Expectation Of A Black-Box Function
Our hypothesis is that an optimal BODE should be maximizing the expected information gain in the QoI.
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
One, thus, tries to construct a cheap-to-evaluate surrogate model to replace the forward model solver.
Variational Reformulation of Bayesian Inverse Problems
The classical approach to inverse problems is based on the optimization of a misfit function.
