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DOE OSTI2026

Measuring the neutrino-oxygen neutral current quasielastic cross section using the accelerator neutrino neutron interaction experiment

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton gadolinium-doped water Cherenkov detector located on-axis to Fermilab’s Booster Neutrino Beam (BNB). ANNIE is uniquely positioned to perform high-statistics measurements of neutrino-nucleus interactions in water, benefiting from a large neutrino flux due to a short (100-meter) baseline. A central focus of ANNIE’s physics program is the measurement of both charged current (CC) and neutral current (NC) cross sections on water, including neutral current quasielastic (NCQE) and CC-inclusive channels. The NCQE measurement is particularly critical for constraining uncertainties in rare-event searches such as the Diffuse Supernova Neutrino Background (DSNB), where atmospheric $\nu$NCQE interactions constitute a significant and poorly constrained background. This dissertation presents a measurement of the flux-averaged neutrino-oxygen neutral current quasielastic ($\nu$NCQE) cross section using $2.573 \times 10^{20}$~POT of BNB exposure from the 2022 and 2023 beam years. The $\nu$NCQE interaction is identified through the primary $\gamma$-rays produced by nuclear de-excitation of the residual $^{15}$N$^*$ or $^{15}$O$^*$ nucleus following nucleon knockout from $^{16}$O. A dedicated Monte Carlo (MC) re-tuning campaign was conducted using an americium-beryllium (AmBe) calibration source, Michel electrons from stopped muons, and throughgoing dirt muons originating upstream of the detector. This multi-sample approach provided a wide-ranging $\mathcal{O}(\text{MeV})$--$\mathcal{O}(\text{GeV})$ dataset for tuning the simulated detector response, which was subsequently validated against AmBe neutron and Michel electron data for use in the $\nu$NCQE analysis. A dedicated laser calibration campaign was carried out to reduce timing uncertainties across the PMT system, enabling reconstruction of the BNB bunch substructure with sufficient resolution to serve as a background rejection tool. By selecting events in-time with individual neutrino bunches, beam-correlated $\nu$NCQE events are separated from diffuse and accelerator-induced backgrounds, notably skyshine neutrons and externally-originating events, that would otherwise dominate traditional charge-based selections within a small-scale, surface-level, short-baseline detector. A data-driven estimation of the skyshine neutron and external background rates was performed and incorporated into the systematic uncertainty budget. The flux-averaged $\nu$NCQE cross section on oxygen is measured to be $1.57 \pm 0.06\,(\text{stat.})$ $^{+0.91}_{-0.67}\,(\text{syst.})$ $\times 10^{-38}\ \text{cm}^{2}$. A full systematic budget is constructed by propagating uncertainties in the secondary hadronic interaction modeling, background cross section normalizations, detector response, neutrino flux, and the primary $\gamma$-ray emission probabilities from oxygen nuclear de-excitation. An idealized de-excitation model, constructed from existing measurements in the literature is developed to benchmark the predictions of the \textsc{GENIE} event generator. A comparison reveals that \textsc{GENIE} systematically overpredicts the primary $\gamma$-ray emission probability from oxygen de-excitation by a factor of $1.49\times$ for $E_\gamma > 6$~MeV and $3.07\times$ in the $3$--$6$~MeV band. This comparison motivates the dominant systematic uncertainty in this analysis, where a conservative uncertainty of $^{+39.9\%}_{-0\%}$ on the primary $\gamma$-ray signal prediction is assigned. The ANNIE result is consistent with and complementary to existing flux-averaged $\nu$NCQE cross section measurements from T2K and Super-Kamiokande, providing an independent measurement with a different detector, neutrino beam, and analysis methodology. Looking ahead, an upgrade to the ANNIE DAQ infrastructure enabling continuous extended readout will allow a complementary $\nu$NCQE neutron multiplicity measurement, directly relevant to constraining the NCQE background in DSNB searches, competitive with the recent T2K measurement at SK-Gd. The planned Super-SANDI upgrade, deploying a large Water-based Liquid Scintillator (WbLS) volume, will further extend ANNIE's reach to hadronic final states and exclusive NC channels, and enable joint measurements with liquid argon detectors sharing the BNB beamline ahead of DUNE and Hyper-Kamiokande.

Doran, Steven [Iowa State U.]↗

DOE OSTI2025

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗

NASA NTRS2021

ACIX-Aqua: A Global Assessment of Atmospheric Correction Methods for Landsat-8 and Sentinel-2 Over Lakes, Rivers, and Coastal Waters

Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA – ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (ρ̂w). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. This volume of data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errors in ρ̂w560 and ρ̂w664 were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15–30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concentration of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20–30% uncertainties in ρ̂w490≤λ≤743nm yielded 25–70% uncertainties in derived Chla and TSS products for top-performing AC processors. We summarize our results using performance matrices guiding the satellite user community through the OWT-specific relative performance of AC processors. Our analysis stresses the need for better representation of aerosols, particularly absorbing ones, and improvements in corrections for sky- (or sun-) glint and adjacency effects, in order to achieve higher quality downstream products in freshwater and coastal ecosystems.

Nima Pahlevan↗

NASA NTRS2015

Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-Bounded Flows

The goal of this work was to quantify the uncertainty and sensitivity of commonly used turbulence models in Reynolds-Averaged Navier-Stokes codes due to uncertainty in the values of closure coefficients for transonic, wall-bounded flows and to rank the contribution of each coefficient to uncertainty in various output flow quantities of interest. Specifically, uncertainty quantification of turbulence model closure coefficients was performed for transonic flow over an axisymmetric bump at zero degrees angle of attack and the RAE 2822 transonic airfoil at a lift coefficient of 0.744. Three turbulence models were considered: the Spalart-Allmaras Model, Wilcox (2006) k-w Model, and the Menter Shear-Stress Trans- port Model. The FUN3D code developed by NASA Langley Research Center was used as the flow solver. The uncertainty quantification analysis employed stochastic expansions based on non-intrusive polynomial chaos as an efficient means of uncertainty propagation. Several integrated and point-quantities are considered as uncertain outputs for both CFD problems. All closure coefficients were treated as epistemic uncertain variables represented with intervals. Sobol indices were used to rank the relative contributions of each closure coefficient to the total uncertainty in the output quantities of interest. This study identified a number of closure coefficients for each turbulence model for which more information will reduce the amount of uncertainty in the output significantly for transonic, wall-bounded flows.

Schaefer, John↗

DOE OSTI2020

An Uncertainty Management Framework for Integrated Gas-Electric Energy Systems

In many parts of the world, electric power systems have seen a significant shift toward generation from renewable energy and natural gas. Because of their ability to flexibly adjust power generation in real time, gas-fired power plants are frequently seen as the perfect partner for variable renewable generation. However, this reliance on gas generation increases interdependence and propagates uncertainty between power grids and gas pipelines and brings coordination and uncertainty management challenges. To address these issues, we propose an uncertainty management framework for uncertain, but bounded gas consumption by gas-fired power plants. The admissible ranges are computed based on a joint optimization problem for the combined gas and electricity networks, which involves chance-constrained scheduling for the electric grid and a novel robust optimization formulation for the natural-gas network. This formulation ensures feasibility of the integrated system with a high probability, while providing a tractable numerical formulation. A key advance with respect to existing methods is that our method is based on a physically accurate, validated model for transient gas pipeline flows. Our case study benchmarks our proposed formulation against methods that ignore how reserve activation impacts the fuel use of gas power plants and only consider predetermined gas consumption. Here, the results demonstrate the importance of considering uncertainty to avoid operating constraint violations and curtailment of gas to the generators.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Effects of cluster expansion on the locations of phase transition boundary as a first step to quantify uncertainty in first principles statistical mechanics framework

Predicting phase diagrams from first principle calculations eliminates the need of tedious experimental trials and errors. Fully automating first principle phase diagram calculations without any sort of human intervention has been a long daunting task and troubling scientific communities for decades. This grand problem remains not fully resolved, largely due to the vastly high-dimensional parameter space associated with density functional theory, cluster expansion, lattice Monte Carlo, and the substantial uncertainty propagating through a set of complex simulations. As a first step to tackle this grand problem, we reported a first demonstration of how sensitive phase boundary locations can be to various cluster expansion fittings and input DFT training data. To the best knowledge of the authors, this study reported the first ever attempt to quantify uncertainty in first principles statistical mechanics framework. In addition, a semi-automated phase transition detection algorithm has been devised in this paper to deal with the associated statistical errors and uncertainties from Monte Carlo method and its predecessor DFT calculations and cluster expansions. This algorithm has been applied in a classical cluster expansion Mg-Cd binary alloy system to detect phase transitions at various locations of phase diagram using different cluster expansions to demonstrate its predictive power and quantify uncertainties. The results suggested that using Chebyshev basis function shifted the transition locations toward the dilute solid solution phase and expanded the phase stability of concentrated ordered phases to wider composition range. The addition of perturbed defect configurations into training data set lowered the order-disorder transition temperature to be away from true transition temperature, suggesting the transition nature is indeed configurational disorder dominated rather than defect assisted. Finally, we found that the weighting has negligible effect on the transition locations, except for the case of using Chebyshev basis function to fit all non-weighted configurations that can be susceptible to Monte Carlo sampling hysteresis.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A Systems Approach to Estimating the Uncertainty Limits of X-Ray Radiographic Metrology

Micro- and nanomanufacturing capabilities have rapidly expanded over the past decade to include complex three-dimensional (3D) structure fabrication; however, the metrology required to accurately assess these processes via part inspection and characterization has struggled to keep pace. X-ray computed tomography (CT) is considered an ideal candidate for providing the critically needed metrology on the smallest scales, especially internal features, or inaccessible regions. X-ray CT supporting micro- and nanomanufacturing often push against the poorly understood resolution and variation limits inherent to the machines, which can distort or hide fine structures. In this study, we have developed and experimentally verify a comprehensive analytical uncertainty propagation signal variation flow graph (SVFG) model for X-ray radiography in this work to better understand resolution and image variability limits on the small scale. The SVFG approach captures, quantifies, and predicts variations occurring in the system that limit metrology capabilities, particularly in the micro/nanodomain. This work is the first step to achieving full uncertainty modeling of CT reconstructions and provides insight into improving X-ray attenuation imaging systems. The SVFG methodology framework is applied to generate a complete basis set of functions describing the major sources of variation in radiographs. Five models are identified, covering variation in energy, intensity, length, blur, and position. Radiographic system experiments are defined to measure the parameters required by the SVFGs. Best practices are identified for these measurements. The SVFG models are confirmed via direct measurement of variation to predict variation within 30% on average.

47 OTHER INSTRUMENTATION↗

NASA NTRS1994

A Monte Carlo study of photochemical model uncertainties affecting the calculation of subsonic perturbations in the upper troposphere

Monte Carlo methods are frequently applied to the evaluation of uncertainties in models with multiple inputs that themselves have associated imprecisions. In the case of photochemical models used to evaluate changes in O3 or OH, inputs analyzed include rate coefficients measured in the laboratory, and chemical and physical constituents measured in the atmosphere. The Monte Carlo method was used with the 1-dimensional GSFC tropospheric photochemical model to examine uncertainty propagation to calculation of NO(x) and the major oxidants (O3, OH) in the upper troposphere. In all cases chemical kinetics inputs are varied and the NO(x) perturbation of a subsonic fleet is simulated. A series of model runs is used to explore sensitivities of model-computed parameters to other parameters: heterogeneous processes, the uncertainty in upper tropospheric H2O vapor measurements, aircraft emissions at different latitudes.

Thompson, Anne M.↗

DOE OSTI2024

Sensor Qualification for the Development of Advanced Reactors

A temperature sensor qualification device is undergoing development and testing in order to qualify temperature sensors for advanced reactor metrology applications. This device will provide National Institute of Standards and Technology (NIST) traceable performance testing of temperature sensors while under the high radiation environments experienced while in-core. This is completed by utilizing retractable sensors which transfer the measurements from ex-core to in-core sensors. A detailed discussion of the uncertainty propagation associated with this comparative process is provided. Experimental testing of the temperature distribution occurring in the device has begun and is reported on. It is pertinent to minimize this temperature distribution to prevent variation in sensor measurements. This variation will be indistinguishable from sensor drift for in-pile applications and would result in unnecessarily conservative uncertainty bounds.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2020

Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification

This report summarizes a joint effort between Argonne National Laboratory, Idaho National Laboratory, and Los Alamos National Laboratory to develop and deploy constitutive models targeted at predicting the life of Grade 91 alloy components subjected to high temperature environments typical of those that structural components in advanced nuclear reactors would experience. Two distinct, but complementary constitutive modeling approaches have been taken here. The first employs a phenomenological viscoplastic model for which parameters have been calibrated based on experimental data for a wide range of Grade 91 alloy that has undergone a variety of processing. A Bayesian approach was used to derive distributions of uncertain parameters for this model based on this data set. The second approach is a reduced order model suitable for engineering-scale analysis that is based on the results of a large set of mesoscale simulations. Mesoscale models allow for the microstructure and composition of a particular alloy to be directly taken into account in the computation of the viscoplastic response, but are computationally expensive, which makes it impractical to directly call those models for the material constitutive response in an engineering-scale simulation. The reduced-order representation of the response of the underlying model used here allows for an engineering-scale model to take into account the characteristics of the underlying microstructure, while only incurring a reasonable computational expense. Both of these approaches have different strengths, and are applicable for different parts of the design/analysis process. The phenomenological models can be readily parameterized based on a set of experimental data for a given class of materials and used for scoping calculations. Once a specific material is chosen and adequately characterized, the reduced order models can accurately predict the response of that specific alloy, and because the models are based on predictive models of the underlying microstructure, they can be used to more confidently predict the response under conditions in regions where there is limited experimental data. Both of these models have been integrated in the Grizzly code, which is used here to perform proof-of-concept uncertainty quantification analyses of a simple component under prototypical conditions. The built- in stochastic analysis capabilities in the MOOSE framework that Grizzly is built on are used here to run large sets of simulations for this uncertainty quantification analysis. As would be expected, because the reduced order models are developed for a much more tightly defined alloy, they predict tighter distributions of the time to failure than the phenomenological models, which are calibrated to a broader set of data. Also important is that these simulations demonstrate that a reduced order modeling approach can be successfully deployed to propagate uncertainties from the material scale to practical engineering-scale component simulations.

42 ENGINEERING↗

DOE OSTI2023

(U) Correlated Sampling Using Batch Statistics to Reduce the Uncertainty of Combinations of KSEN Outputs with MCNP6

The relative sensitivity of k eff to the densities of nuclides in a material are combined to compute relative sensitivities to other inputs. When computed in a single Monte Carlo run, the nuclide density sensitivities are correlated, and the statistical uncertainties propagated to other inputs will be incorrect unless those correlations are accounted for. Equations are presented to apply correlated sampling using batch statistics for the sum of an arbitrary number of random tallies and the difference of two random tallies when each is multiplied by a different constant. When correlated sampling is used on a recent benchmark evaluation, the correct statistical uncertainties for certain combinations of sensitivities are dramatically smaller than the incorrect uncertainties. A user-controlled, regular output of MCNP6’s KSEN sensitivities that allows batch statistics to be applied to combinations would be extremely valuable.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2018

Stochastic Reduced Order Models with Python (SROMPy)

Stochastic Reduced Order Models with Python (SROMPy) is a software package developed to enable user-friendly utilization of the stochastic reduced order model (SROM) approach for uncertainty quantification. A SROM is a low dimensional, discrete approximation to a random quantity that enables efficient and non-intrusive stochastic computations. With SROMPy, a user can easily generate a SROM to approximate a random variable or vector described by several different types of probability distributions using the Python programming language. Once a SROM is constructed, the software can be used to propagate uncertainty through a user-defined computational model to estimate statistics of a given quantity of interest. This report is meant to introduce the SROMPy module and brie y demonstrate its capabilities. A simple example of a spring-mass system with a random input is included to illustrate the practicality of the SROM approach to uncertainty quantification and relative ease of applying it with SROMPy. The example includes a comparison with a solution obtained using classical Monte Carlo simulation, demonstrating the similarities and advantages of using the SROM approach.

Warner, James E.↗

DOE OSTI2020

Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

One of the open problems in scientific computing is the long-time integration of nonlinear stochastic partial differential equations (SPDEs), especially with arbitrary initial data. We address this problem by taking advantage of recent advances in scientific machine learning and the spectral dynamically orthogonal (DO) and borthogonal (BO) methods for representing stochastic processes. The recently introduced DO/BO methods reduce the SPDE to solving a system of deterministic PDEs and a system of stochastic ordinary differential equations. Specifically, we propose two new physics-informed neural networks (PINNs) for solving time-dependent SPDEs, namely the neural network (NN)-DO/BO methods. The proposed methods incorporate the DO/BO constraints into the loss function (along with the modal decomposition of the SPDE) with an implicit form instead of generating explicit expressions for the temporal derivatives of the DO/BO modes. Hence, the NN-DO/BO methods can overcome some of the drawbacks of the original DO/BO methods. For example, we do not need the assumption that the covariance matrix of the random coefficients is invertible as in the original DO method, and we can remove the assumption of no eigenvalue crossing as in the original BO method. Moreover, the NN-DO/BO methods can be used to solve time-dependent stochastic inverse problems with the same formulation and same computational complexity as for forward problems. Furthermore, we demonstrate the capability of the proposed methods via several numerical examples, namely: (1) A linear stochastic advection equation with deterministic initial condition: we obtain good results with the proposed methods, while the original DO/BO methods cannot be applied directly in this case. (2) Long-time integration of the stochastic Burgers' equation: we show the good performance of NN-DO/BO methods, especially the effectiveness of the NN-BO approach for such problems with many eigenvalue crossings during the whole time evolution, while the original BO method fails. (3) Nonlinear reaction diffusion equation: we consider both the forward problem and the inverse problems, including very noisy initial point values, to investigate the flexibility of the NN-DO/BO methods in handling inverse and mixed type problems. Taken together, these simulation results demonstrate that the NN-DO/BO methods can be employed to effectively quantify uncertainty propagation in a wide range of physical problems, but future work should address the efficiency issue of PINNs for forward problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Evaluation of the mean excitation energy of liquid argon

Current and future experiments need to know the stopping power of liquid argon. It is used directly in calibration, where commonly the minimum-ionizing portion of muon tracks is used as a standard candle. Similarly, muon range is used as a measure of muon energy. More broadly, the stopping power figures into the simulation of all charged particles, and so uncertainty propagates widely throughout data analysis of all sorts. The main parameter that controls stopping power is the mean excitation energy, or I-value. Experimental information for argon's I-value come primarily from measurements of gaseous argon, with a very limited amount of information from solid argon, and none from liquid argon. These measurements are reviewed, along with a discussion of the difference in central value and uncertainty incurred by the difference of phase. The values newly recommended by this note are $(188 \pm 6)$ eV for gaseous argon and $(197 \pm 8)$ eV for liquid argon (68% C.L. in each case).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRSDate not supplied

Uncertainty Quantification and Sensitivity Analysis in Process-Structure-Property Simulations for Laser Powder Bed Fusion Additive Manufacturing

Process variations and process-induced defects like porosity cause significant uncertainty in the microstructure and mechanical behavior of additively manufactured metals. Establishing process-structure-property (PSP) relationships and quantifying uncertainty using experiments alone is costly, especially for structural applications where mechanical allowables must be established for qualification and certification. This work presents a PSP simulation framework for laser powder bed fusion with a focus on uncertainty quantification through probabilistic calibration and multi-fidelity uncertainty propagation. Motivated by phenomenological input parameters related to grain nucleation and growth that are difficult to characterize, a global sensitivity analysis (GSA) is completed. Through GSA, the most important input parameters are identified based on their influence on the statistical distributions of microstructural metrics that influence mechanical behavior, including grain size, morphology, and crystallographic texture. The results provide insight on what experiments are necessary to quantify and control PSP uncertainties, particularly those associated with the more challenging input parameters.

additive manufacturing↗

DOE OSTI2020

Quantifying uncertainties on fission fragment mass yields with mixture density networks

Probabilistic machine learning techniques can learn both complex relations between input features and output quantities of interest as well as take into account stochasticity or uncertainty within a data set. In this initial work, we explore the use of one such probabilistic network, the mixture density network (MDN), to reproduce fission yields and their uncertainties. In this paper, we study mass yields for the spontaneous fission of 252 Cf, exploring the number of training samples needed for converged predictions, how different levels of uncertainty propagate from the training set to the MDN predictions, and how well physical constraints of the yields—such as normalization and symmetry—are upheld by the algorithm. Finally, we test the ability of the MDN to interpolate between and extrapolate beyond samples in the training set using energy-dependent mass yields for the neutron-induced fission on 235 U. The MDN provides a reliable way to include and predict uncertainties and is a promising path forward for supplementing sparse sets of nuclear data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Estimating epistemic uncertainty in soil parameters for nonlinear site response analyses: Introducing the Latin Hypercube Sampling technique

This study quantifies the effects of epistemic uncertainty in soil parameters on nonlinear (NL) site response analysis (SRA) results, validated against the data recorded at a well-instrumented geotechnical downhole array located in Japan. To this end, a one-dimensional soil column model of the Service Hall Array (SHA) near the Kashiwazaki-Kariwa Nuclear Power Plant (KKNPP) is developed using the finite element (FE) program LS-DYNA. The dynamic stress–strain relationship is characterized by a modified two-stage hyperbolic (MTH) NL backbone curve formulation capable of capturing soil behavior at both small- and large-shear strains. The model is then validated against the ground motion recordings to capture the model bias. The uncertainties associated with the shear-wave velocity profile (a small-strain soil property) and soil shear strength (a large-strain soil property) are incorporated in NL SRA to quantify their separate and joint randomization effects on the results. This study proposes using the Latin Hypercube Sampling (LHS) method as an efficient alternative to commonly used methods, such as Standard Monte Carlo (SMC), to account for uncertainty propagation in such reliability analysis. Both low-intensity and design-level records from the recordings at the SHA are applied to study the contribution of the small- and large-strain NL dynamic soil properties. Results from 46,200 NL FE analyses (23,100 per input motion) are presented. Measured and predicted site response, using recorded ground motions at this downhole array, is compared to assess the significance of soil parameter uncertainty on the observed ground motion dispersions. It is demonstrated that increasing the number of soft realizations and implementing higher level earthquake intensity lead to higher ground motion dispersion. Unlike past studies in randomization of Vs profiles with the SMC method, the LHS method is shown to have no significant effect on the predicted median surface response spectra and amplification factors (AFs) for this case study.

Engineering↗

NASA NTRSDate not supplied

Uncertainty Quantification using Deep Ensembles for Decision Making in Cyber-Physical-Human Systems

In this paper and its companion, Differential Equation Approximation Using Gradient-Boosted Quantile Regression, Robison et al., we examine an approach to quantifying model uncertainty with the aim of increasing the trustworthiness of computational models in human-machine interactions. In Differential Equation Approximation Using Gradient-Boosted Quantile Regression, we focus on gradient-boosted decision trees, while in this one, we give more details about deep ensembles. Uncertainty quantification is crucial for building trustworthy autonomous decision-making agents in human-machine teams. There are two types of uncertainties: aleatoric and epistemic. The former is related to the inherent stochasticity (noise) of the process, whereas the latter is associated with the lack of knowledge or representation capability of models, such as neural networks. By lack of knowledge, we mean the model’s inability to accurately predict outputs for all possible inputs. The aleatory uncertainty can be estimated fairly easily with, for example, filters, whereas epistemic uncertainty is challenging to compute. This paper uses deep ensembles to quantify both aleatory and epistemic uncertainty. It can act as an uncertainty-aware surrogate transition model for decision-making frameworks. "Uncertainty-aware" means that the surrogate transition model should make predictions along with confidence in those predictions. In the context of decision-making, the transition models are ordinary differential equations (ODEs). Since ODEs can be simulated to make one-step or multi-step predictions, a good surrogate model for them should perform reasonably well in both modes. In a multi-step approach, the trajectory sampling method TS∞ was used to propagate uncertainty over multiple steps. The cartpole dynamical system was selected to demonstrate the ability of deep ensembles as good surrogate transition models for decision-making frameworks. The deep ensembles modeled the dynamics of cartpole ODEs and made uncertainty-aware predictions in single-step and multi-step transition modes.

CPH systems↗