A Parallel Transitional MCMC for Robust PDF Sampling - New UQTk Capability
Abstract not provided.
Publications
Sparse PCE surrogate assisted inversion algorithm for ultra-deep electromagnetic resistivity logging-while-drilling data
Abstract not provided.
Compressive sensing adaptation for polynomial chaos expansions
Journal of Computational Physics
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ. Several rotations have been proposed in the literature resulting in adaptations with different convergence properties. In this paper we present a new adaptation mechanism that builds on compressive sensing algorithms, resulting in a reduced polynomial chaos approximation with optimal sparsity. The developed adaptation algorithm consists of a two-step optimization procedure that computes the optimal coefficients and the input projection matrix of a low dimensional chaos expansion with respect to an optimally rotated basis. We demonstrate the attractive features of our algorithm through several numerical examples including the application on Large-Eddy Simulation (LES) calculations of turbulent combustion in a HIFiRE scramjet engine.
Exploiting Low-Rank Structure for Sensitivity Analysis in Earth System Models
Abstract not provided.
Workflows for Quantifying Uncertainty in Computational Model Predictions
Abstract not provided.
UQTk A Flexible Python/C++ Toolkit for Uncertainty Quantification
Abstract not provided.
An Overview of Training Data Security Vulnerabilities: Machine Learning is a Leaky Black Box
Abstract not provided.
Enhancing model predictability for a scramjet using probabilistic learning on manifolds
AIAA Journal
The computational burden of a large-eddy simulation for reactive flows is exacerbated in the presence of uncertainty in flow conditions or kinetic variables. A comprehensive statistical analysis, with a sufficiently large number of samples, remains elusive. Statistical learning is an approach that allows for extracting more information using fewer samples. Such procedures, if successful, will greatly enhance the predictability of models in the sense of improving exploration and characterization of uncertainty due to model error and input dependencies, all while being constrained by the size of the associated statistical samples. In this paper, it is shown how a recently developed procedure for probabilistic learning on manifolds can serve to improve the predictability in a probabilistic framework of a scramjet simulation. The estimates of the probability density functions of the quantities of interest are improved together with estimates of the statistics of their maxima. It is also demonstrated how the improved statistical model adds critical insight to the performance of the model.
Progress in Scramjet Design Optimization Under Uncertainty Using Simulations of the HIFire Configuration
Abstract not provided.
Enhancing statistical moment calculations for stochastic Galerkin solutions with Monte Carlo techniques
Journal of Computational Physics
In this work, we provide a method for enhancing stochastic Galerkin moment calculations to the linear elliptic equation with random diffusivity using an ensemble of Monte Carlo solutions. This hybrid approach combines the accuracy of low-order stochastic Galerkin and the computational efficiency of Monte Carlo methods to provide statistical moment estimates which are significantly more accurate than performing each method individually. The hybrid approach involves computing a low-order stochastic Galerkin solution, after which Monte Carlo techniques are used to estimate the residual. We show that the combined stochastic Galerkin solution and residual is superior in both time and accuracy for a one-dimensional test problem and a more computational intensive two-dimensional linear elliptic problem for both the mean and variance quantities.
Exploiting Model Structure for Global Sensitivity Analysis in E3SM Land Model
Abstract not provided.
Uncertainty Propagation Using Conditional Random Fields in Large-Eddy Simulations of Scramjet Computations
Abstract not provided.
Coupling FAGE Detection of OH and HO2 with a High-Pressure Reactor for Biofuel Characterization
Abstract not provided.
Interatomic Potentials Models for Cu-Ni and Cu-Zr Alloys
This study explores a Bayesian calibration framework for the RAMPAGE alloy potential model for Cu-Ni and Cu-Zr systems, respectively. In RAMPAGE potentials, it is proposed that once calibrated potentials for individual elements are available, the inter-species interac- tions can be described by fitting a Morse potential for pair interactions with three parameters, while densities for the embedding function can be scaled by two parameters from the elemen- tal densities. Global sensitivity analysis tools were employed to understand the impact each parameter has on the MD simulation results. A transitional Markov Chain Monte Carlo al- gorithm was used to generate samples from the multimodal posterior distribution consistent with the discrepancy between MD simulation results and DFT data. For the Cu-Ni system the posterior predictive tests indicate that the fitted interatomic potential model agrees well with the DFT data, justifying the basic RAMPAGE assumtions. For the Cu-Zr system, where the phase diagram suggests more complicated atomic interactions than in the case of Cu-Ni, the RAMPAGE potential captured only a subset of the DFT data. The resulting posterior distri- bution for the 5 model parameters exhibited several modes, with each mode corresponding to specific simulation data and a suboptimal agreement with the DFT results.
Bayesian Estimation of Pollutant Emissions using Multiscale Data
Abstract not provided.
Adaptive Sparse Quadrature for Multifidelity Scramjet Simulations
Abstract not provided.
Using OH and HO2 Radicals as Markers of Biofuel Ignition Properties
Abstract not provided.
Bayesian Estimation of Spatio-Temporal Parameters for Atmospheric Transport Models
Abstract not provided.
Chance-constrained economic dispatch with renewable energy and storage
Computational Optimization and Applications
Increasing penetration levels of renewables have transformed how power systems are operated. High levels of uncertainty in production make it increasingly difficulty to guarantee operational feasibility; instead, constraints may only be satisfied with high probability. We present a chance-constrained economic dispatch model that efficiently integrates energy storage and high renewable penetration to satisfy renewable portfolio requirements. Specifically, we require that wind energy contribute at least a prespecified proportion of the total demand and that the scheduled wind energy is deliverable with high probability. We develop an approximate partial sample average approximation (PSAA) framework to enable efficient solution of large-scale chance-constrained economic dispatch problems. Computational experiments on the IEEE-24 bus system show that the proposed PSAA approach is more accurate, closer to the prescribed satisfaction tolerance, and approximately 100 times faster than standard sample average approximation. Finally, the improved efficiency of our PSAA approach enables solution of a larger WECC-240 test system in minutes.
Machine Learning Techniques for Global Sensitivity Analysis in Earth System Models
Abstract not provided.
Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions
Abstract not provided.
UQTk: A Flexible Python/C++ Toolkit for Uncertainty Quantification
Abstract not provided.
Sparse multifidelity approximations for forward UQ
Abstract not provided.
Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations
AIAA Journal
The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the system’s stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. Finally, these methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.
Scalable Uncertainty Quantification: Exploiting Structure in Models and Data
Abstract not provided.
Results 51–75 of 239