Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (Theory Manual)

Dalbey, Keith; Eldred, Michael; Geraci, Gianluca; Jakeman, John Davis; Maupin, Kathryn; Monschke, Jason; Seidl, Daniel; Tran, Anh; Menhorn, Friedrich; Zeng, Xiaobo

doi:10.2172/1784843

Cited by 16 publications

(10 citation statements)

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“…Tools like Dakota [12] with sampling approaches like incremental sampling, importance sampling, and adaptive sampling have been long available for uncertainty quantification in engineering design. Such samplers have recently gained interest for sampling simulation-based scenes in the AV domain.…”

Section: Related Workmentioning

confidence: 99%

“…Samplers are used to generate data across the search space created by the system parameters. For example, tools like Dakota [12] provide efficient samplers like incremental sampling, importance sampling, and adaptive sampling for uncertainty quantification in engineering design. Recently, this concept of sampling-based data generation is being adapted for autonomous systems [8], [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

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Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems

Ramakrishna¹,

Luo²,

Barve³

et al. 2022

Preprint

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Autonomous Cyber-Physical Systems must often operate under uncertainties like sensor degradation and shifts in the operating conditions, which increases its operational risk. Dynamic Assurance of these systems requires designing runtime safety components like Out-of-Distribution detectors and risk estimators, which require labeled data from different operating modes of the system that belong to scenes with adverse operating conditions, sensors, and actuator faults. Collecting real-world data of these scenes can be expensive and sometimes not feasible. So, scenario description languages with samplers like random and grid search are available to generate synthetic data from simulators, replicating these real-world scenes. However, we point out three limitations in using these conventional samplers. First, they are passive samplers, which do not use the feedback of previous results in the sampling process. Second, the variables to be sampled may have constraints that are often not included. Third, they do not balance the tradeoff between exploration and exploitation, which we hypothesize is necessary for better search space coverage. We present a scene generation approach with two samplers called Random Neighborhood Search (RNS) and Guided Bayesian Optimization (GBO), which extend the conventional random search and Bayesian Optimization search to include the limitations. Also, to facilitate the samplers, we use a risk-based metric that evaluates how risky the scene was for the system. We demonstrate our approach using an Autonomous Vehicle example in CARLA simulation. To evaluate our samplers, we compared them against the baselines of random search, grid search, and Halton sequence search. Our samplers of RNS and GBO sampled a higher percentage of high-risk scenes of 83% and 92%, compared to 56% 66% and 71% of the grid, random and Halton samplers, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems

Ramakrishna¹,

Luo²,

Barve³

et al. 2022

Preprint

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“…Numerous choices of collocation points are possible, such as Gauss-Legendre, Clenshaw-Curtis, Leja, and Gauss-Patterson Nobile et al (2016). In this work, the weakly-nested Gaussian abscissas (see Section 3.6.2 of Dalbey et al (2021) and Eldred and Burkardt (2009)), which are zeros of orthogonal polynomials, are utilized for quadrature.…”

Section: Stochastic Collocationmentioning

confidence: 99%

“…Table I, Chapter 5, pg 185) and Wenk and Van Houtte (2004)(Tables 1 and 2), Raabe et al Raabe et al (2002), Raabe and Roters Raabe and Roters (2004), Pham et al Pham et al (2017), and Mangal and Holm Mangal and Holm (2018). We limit the scope of UQ on the microstructuremechanical property linkage, where the crystal plasticity finite element is widely regarded as the microstructure-aware multi-scale ICME model, and adopt the DREAM.3D Groeber and Jackson (2014) and DAMASK Roters et al (2019) workflow as previously demonstrated by Diehl et al Diehl et al (2017), as well as two constitutive models described in Sedighiani et al Sedighiani et al (2020, to perform uncertainty quantification via DAKOTA Eldred (2009); Dalbey et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Microstructure-sensitive uncertainty quantification for crystal plasticity finite element constitutive models using stochastic collocation methods

Tran¹,

Wildey²,

Lim³

2022

Preprint

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Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allows numerical predictions, providing the bridge from microstructure to materials properties and performances. Several constitutive models have been proposed in the last few decades to capture the mechanics and plasticity behavior of materials. While some UQ studies have been performed, the robustness and uncertainty of these constitutive models have not been rigorously established. In this work, we apply a stochastic collocation (SC) method, which is mathematically rigorous and has been widely used in the field of UQ, to quantify the uncertainty of three most commonly used constitutive models in CPFEM, namely phenomenological models (with and without twinning), and dislocationdensity-based constitutive models, for three different types of crystal structures, namely face-centered cubic (fcc) copper (Cu), body-centered cubic (bcc) tungsten (W), and hexagonal close packing (hcp) magnesium (Mg). Our numerical results not only quantify the uncertainty of these constitutive models in stress-strain curve, but also analyze the global sensitivity of the underlying constitutive parameters with respect to the initial yield behavior, which may be helpful for robust constitutive model calibration works in the future.

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“…mechanical property linkage, where the crystal plasticity finite element is widely regarded as the microstructure-aware multiscale ICME model, and adopt the DREAM.3D Groeber and Jackson (2014) and DAMASK Roters et al (2019) workflow as previously demonstrated by Diehl et al (2017), as well as two constitutive models described in Sedighiani et al (2020Sedighiani et al ( , 2022, to perform uncertainty quantification via DAKOTA Eldred (2009); Dalbey et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods

Tran

Wildey²,

Lim³

2022

Front. Mater.

Self Cite

View full text Add to dashboard Cite

Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allows numerical predictions, providing the bridge from microstructure to materials properties and performances. Several constitutive models have been proposed in the last few decades to capture the mechanics and plasticity behavior of materials. While some UQ studies have been performed, the robustness and uncertainty of these constitutive models have not been rigorously established. In this work, we apply a stochastic collocation (SC) method, which is mathematically rigorous and has been widely used in the field of UQ, to quantify the uncertainty of three most commonly used constitutive models in CPFEM, namely phenomenological models (with and without twinning), and dislocation-density-based constitutive models, for three different types of crystal structures, namely face-centered cubic (fcc) copper (Cu), body-centered cubic (bcc) tungsten (W), and hexagonal close packing (hcp) magnesium (Mg). Our numerical results not only quantify the uncertainty of these constitutive models in stress-strain curve, but also analyze the global sensitivity of the underlying constitutive parameters with respect to the initial yield behavior, which may be helpful for robust constitutive model calibration works in the future.

show abstract

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (Theory Manual)

Cited by 16 publications

References 0 publications

Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems

Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems

Microstructure-sensitive uncertainty quantification for crystal plasticity finite element constitutive models using stochastic collocation methods

Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods

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