Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Bayesian Optimization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Optimizing Optical Searches for Supermassive Black Hole Binaries in Active Galactic Nuclei Light Curves: Fourier versus Bayesian Periodicity Detection

Simulations predict that supermassive black hole binaries (SMBHBs) will exhibit periodic brightness variations that may exceed the stochastic variability intrinsic to active galactic nuclei (AGN). In this paper, we simulate SMBHBs with damped random walk (DRW) AGN variability and an added sinusoidal signal from the orbital motion, and test three methods—a generalized Lomb–Scargle periodogram (GLSP), a nested Bayesian sampler (NBS), and a weighted wavelet z-transform (or WWZ)—to determine which is best at recovering the periodicity. Our simulated light curves follow the properties of the Catalina Real-Time Transient Survey (or CRTS), Legacy Survey of Space and Time (LSST), and Zwicky Transient Facility (ZTF) to best inform current and future SMBHB searches. We map a broad range of parameter space and identify which DRW-only light curves best mimic periodicity and pass each method’s model selection. The NBS performs best at detecting periodicity and filtering out DRW-only light curves. Combined candidate selection with both the NBS and GLSP significantly reduces false-positive rates (FPRs) with marginal impact on true-positive rates (TPRs). With this joint model selection pipeline, we find the lowest FPRs in ZTF-like simulations and the highest detection rates in LSST-like simulations. Using a modified computation of the false-alarm probability with GLSP, we efficiently triage LSST AGN light curves (∼10 7 light curves in ∼10–30 hr) and achieve TPRs and FPRs of ∼40% and ∼0.5%, respectively.

Banaszak, Sebastian M. [Vanderbilt Univ., Nashvill↗
DOE OSTI2020

Determining Multi-Component Phase Diagrams with Desired Characteristics Using Active Learning

Herein, we demonstrate how to predict and experimentally validate phase diagrams for multi-component systems from a high-dimensional virtual space of all possible phase diagrams involving several elements based on small existing experimental data. The experimental data for bulk phases for known systems represents a sampling from this space, and screening the space allows multi-component phase diagrams with given design criteria to be built. This approach uses machine learning methods to predict phase diagrams and Bayesian experimental design to minimize experiments for refinement and validation, all within an active learning loop. The approach is proven by predicting and synthesizing the ferroelectric ceramic system (1-ω)(Ba 0.61 Ca 0.28 Sr 0.11 TiO 3 )-ω(BaTi 0.888 Zr 0.0616 Sn 0.0028 Hf 0.0476 O 3 ) with a relatively high transition temperature and triple point, as well as the NiTi-based pseudo-binary phase diagram (1-ω)(Ti 0.309 Ni 0.485 Hf 0.20 Zr 0.006 )-ω(Ti 0.309 Ni 0.485 Hf 0.07 Zr 0.068 Nb 0.068 ) designed for high transition temperature (ω ≤ 1). Each phase diagram is validated and optimized through only three new experiments. The complexity of these compounds is beyond the reach of today’s computational methods.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17–18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17-18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2013

Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it-an improved and generalized version of Bayesian Blocks [Scargle 1998]-that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piece- wise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by [Arias-Castro, Donoho and Huo 2003]. In the spirit of Reproducible Research [Donoho et al. (2008)] all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.

signal detection↗
DOE OSTI2020

Calibration of energy density functionals with deformed nuclei

Nuclear density functional theory is the prevalent theoretical framework for accurately describing nuclear properties at the scale of the entire chart of nuclides. Given an energy functional and a many-body scheme (e.g., single- or multireference level), the predictive power of the theory depends strongly on how the parameters of the energy functionals have been calibrated with experimental data. Expanded algorithms and computing power have enabled recent optimization protocols to include data in deformed nuclei in order to optimize the coupling constants of the energy functional. The primary motivation of this work is to test the robustness of such protocols with respect to some of the technical and numerical details of the underlying calculations, especially when the calibration explores a large parameter space. To this end, we quantify the effect of these uncertainties on both the optimization and statistical emulation of composite objective functions. We also emphasize that Bayesian calibration can provide better estimates of the theoretical errors used to define objective functions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types

To improve the viability of nuclear power plants, there is a need to reduce their operational costs. Operational costs account for a significant portion of a plant’s yearly budget, due to their scheduled-based maintenance approach. In order to reduce these costs, proactive methods are required that estimate and forecast the state of a machine in real time to optimize maintenance schedules. In this research, we use Bayesian networks to develop a framework that can forecast the remaining useful life of a centrifugal pump. To do so, we integrate survival analysis with Bayesian statistics to forecast the health of the pump conditional to its current state. We complete our research by successfully using the Bayesian network on a case study. This solution provides an informed probabilistic viewpoint of the pumping system for the purpose of predictive maintenance.

42 ENGINEERING↗
DOE OSTI2021

Bayesian Inference for Integrating Yarrowia lipolytica Multiomics Datasets with Metabolic Modeling

Optimizing the metabolism of microbial cell factories for yields and titers is a critical step for economically viable production of bioproducts and biofuels. In this process, tuning the expression of individual enzymes to obtain the desired pathway flux is a challenging step, in which data from separate multiomics techniques must be integrated with existing biological knowledge to determine where changes should be made. Following a design-build-test-learn strategy, building on recent advances in Bayesian metabolic control analysis, we identify in this work key enzymes in the oleaginous yeast Yarrowia lipolytica that correlate with the production of itaconate by integrating a metabolic model with multiomics measurements. To this extent, we quantify the uncertainty for a variety of key parameters, known as flux control coefficients (FCCs), needed to improve the bioproduction of target metabolites and statistically obtain key correlations between the measured enzymes and boundary flux. Based on the top five significant FCCs and five correlated enzymes, our results show phosphoglycerate mutase, acetyl-CoA synthetase (ACSm), carbonic anhydrase (HCO3E), pyrophosphatase (PPAm), and homoserine dehydrogenase (HSDxi) enzymes in rate-limiting reactions that can lead to increased itaconic acid production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

EFIT‐AI: Machine Learning and Artificial Intelligence Assisted Equilibrium Reconstruction for Tokamak Experiments and Burning Plasmas (Final Report)

The EFIT-AI project is creating a modern advanced equilibrium reconstruction code suitable for tokamak experiments of burning plasmas. EFIT [1,2] was the first and is the most extensively used equilibrium reconstruction code in the world. This project builds on the production-level experience and adds key elements as follows. 1. A Model Order Reduction (MOR) version of the two-dimensional (2D) Grad-Shafranov equation solver (EFIT-MORNN) using physics-informed neural networks. 2. Improved optimization and data analysis capabilities using a Bayesian framework enhanced with machine learning. 3. A MOR version of the three-dimensional (3D) perturbed equilibrium reconstruction tool.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1

Abstract. Runoff is a critical component of the terrestrial water cycle, and Earth system models (ESMs) are essential tools to study its spatiotemporal variability. Runoff schemes in ESMs typically include many parameters so that model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at a global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using a surrogate-assisted Bayesian framework. First, the polynomial chaos expansion machinery with Bayesian compressed sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5∘ × 0.5∘ for 1991–2010. The error metric between the ELM simulations and the benchmark data is selected to construct the surrogates, which facilitates efficient calibration and avoids the more conventional, but challenging, construction of high-dimensional surrogates for the ELM simulated runoff. Second, the Sobol' index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that, in most regions, ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multimodel ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

58 GEOSCIENCES↗
NASA NTRS1994

DTS: Building custom, intelligent schedulers

DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses on how the toolkit might be reused in future NASA mission schedulers. The toolkit includes a user-customizable scheduling interface, and a 'Just-For-You' optimization engine. The customizable interface is built on two metaphors: objects and dynamic graphs. Objects help to structure problem specifications and related data, while dynamic graphs simplify the specification of graphical schedule editors (such as Gantt charts). The interface can be used with any 'back-end' scheduler, through dynamically-loaded code, interprocess communication, or a shared database. The 'Just-For-You' optimization engine includes user-specific utility functions, automatically compiled heuristic evaluations, and a postprocessing facility for enforcing scheduling policies. The optimization engine is based on BPS, the Bayesian Problem-Solver (1,2), which introduced a similar approach to solving single-agent and adversarial graph search problems.

Hansson, Othar↗
NASA NTRS2020

Constraining Fossil Fuel CO2 Emissions from Urban Area Using OCO-2 Observations of Total Column CO2

Satellite observations of the total column dry-air CO2(XCO2) are expected to support the quantification and monitoring of fossil fuel CO2 (ffCO2) emissions from urban areas. We evaluate the utility of the Orbiting Carbon Observatory 2 (OCO-2) XCO2 retrievals to optimize whole-city emissions, using a Bayesian inversion system and high-resolution transport modeling. The uncertainties of constrained emissions related to transport model, satellite measurements, and local biospheric fluxes are quantified. For the first two uncertainty sources, we examine cities of different landscapes: “plume city” located in relatively flat terrain, represented by Riyadh and Cairo; “basin city” located in basin terrain, represented by Los Angeles (LA). The retrieved scaling factors of emissions and their uncertainties show prominent variabilities from track to track, due to the varying meteorological conditions and relative locations of the tracks transecting plumes. To explore the performance of multiple tracks in retrieving emissions, pseudo data experiments are carried out. The estimated least number of tracks required to constrain the total emissions for Riyadh (<10% uncertainty), Cairo (<10%), and LA (<5%) are 5, 8, and 7, respectively. Additionally, to evaluate the impact of biospheric fluxes on derivation of the ffXCO2 enhancements, we conduct simulations for Pearl River Delta metropolitan area. Significant fractions of local XCO2 enhancements associated with local biospheric XCO2 variations are shown, which potentially lead to biased estimates of ffCO2 emissions. We demonstrate that satellite measurements can be used to improve urban ffCO2 emissions with a sufficient amount of measurements and appropriate representations of the uncertainty components.

Xinxin Ye↗
NASA NTRSDate not supplied

Generating a 4D Global CH(4) Product by Assimilating TROPOMI column CH(4) in NASA’s GEOS GCM

Examination of temporal and spatial CH4 variability is crucial for better understanding the human and natural processes driving climate change and ultimately designing mitigation strategies. Here we present an analysis framework that uses NASA’s GEOS General Circulation Model (GCM) to construct a high-resolution, time varying picture of atmospheric CH4 consistent with measurements from a variety of platforms, both in situ and remotely sensed. The resulting time varying atmospheric CH4 product can (i) support interpretation of high-resolution point source detection approaches, (ii) provide reanalysis fields for CH4 and other greenhouse gases, and (iii) supply boundary conditions for regional models. Our approach starts with a set of CH4 emissions from various inventories that have been adjusted to match the global annual growth rate over recent decades. These emissions are transported by the GEOS GCM, which in turn is constrained by meteorology from NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) product. The simulated atmospheric field is compared with CH4 measurements, such as those from the TROPOspheric Monitoring Instrument (TROPOMI), and adjustments calculated following a Bayesian protocol. The accuracy of the resultant optimal atmospheric CH4 field can be demonstrated by its improved agreement (compared to a direct simulation of the CH4 inventories) with a host of independent CH4 measurements, such as those from the Total Carbon Column Observation Network (TCCON) and in situ observations from surface and airborne platforms.

Nikolay V. Balashov↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Combined selection of the dynamic model and modeling error in nonlinear aeroelastic systems using Bayesian Inference

Here, we report a Bayesian framework for concurrent selection of physics-based models and (modeling) error models. We investigate the use of colored noise to capture the mismatch between the predictions of calibrated models and observational data that cannot be explained by measurement error alone within the context of Bayesian estimation for stochastic ordinary differential equations. Proposed models are characterized by the average data-fit, a measure of how well a model fits the measurements, and the model complexity measured using the Kullback–Leibler divergence. The use of a more complex error models increases the average data-fit but also increases the complexity of the combined model, possibly over-fitting the data. Bayesian model selection is used to find the optimal physical model as well as the optimal error model. The optimal model is defined using the evidence, where the average data-fit is balanced by the complexity of the model. The effect of colored noise process is illustrated using a nonlinear aeroelastic oscillator representing a rigid NACA0012 airfoil undergoing limit cycle oscillations due to complex fluid–structure interactions. Several quasi-steady and unsteady aerodynamic models are proposed with colored noise or white noise for the model error. The use of colored noise improves the predictive capabilities of simpler models.

42 ENGINEERING↗
DOE OSTI2021

Sensitivity Analysis of Particle-In-Cell Modeling Parameters in Settling Bed, Bubbling Fluidized Bed and Circulating Fluidized Bed

The objective of the work presented is to perform a preliminary sensitivity analysis of particle-in-cell (PIC) model parameters when applied to settling bed, bubbling fluidized bed, and circulating fluidized bed simulations. These examples correspond to widely different flow conditions commonly seen in chemical engineering applications. Simulations were performed using the PIC method in the open-source software Multiphase Flow with Interphase eXchanges (MFiX) developed by the National Energy Technology Laboratory (NETL). As part of the non-intrusive uncertainty quantification (UQ) analysis, simulation campaigns were generated using Nodeworks. Sampling locations or settings for PIC model parameters were determined using the Latin Hypercube method. Response surfaces were created using radial basis functions (RBF), and Sobol’ indices were estimated to quantify the influence of model parameters on the quantities of interest (QoI). This study marks a first step towards systematically determining optimal ranges for model parameters used in MFiX-PIC. Based on limited experience, it is expected that these values would depend strongly on flow conditions. Given the complexity of the multiphase flow systems under analysis, a non-intrusive UQ based approach is used to identify the most influential parameters in each case. This prior knowledge will help in proposing an effective design of experiments (DoE) and determine optimal parameters through techniques such as deterministic or Bayesian calibration, which will be pursued in the future.

42 ENGINEERING↗
NASA NTRS1994

An error covariance model for sea surface topography and velocity derived from TOPEX/POSEIDON altimetry

In order to facilitate the use of satellite-derived sea surface topography and velocity oceanographic models, methodology is presented for deriving the total error covariance and its geographic distribution from TOPEX/POSEIDON measurements. The model is formulated using a parametric model fit to the altimeter range observations. The topography and velocity modeled with spherical harmonic expansions whose coefficients are found through optimal adjustment to the altimeter range residuals using Bayesian statistics. All other parameters, including the orbit, geoid, surface models, and range corrections are provided as unadjusted parameters. The maximum likelihood estimates and errors are derived from the probability density function of the altimeter range residuals conditioned with a priori information. Estimates of model errors for the unadjusted parameters are obtained from the TOPEX/POSEIDON postlaunch verification results and the error covariances for the orbit and the geoid, except for the ocean tides. The error in the ocean tides is modeled, first, as the difference between two global tide models and, second, as the correction to the present tide model, the correction derived from the TOPEX/POSEIDON data. A formal error covariance propagation scheme is used to derive the total error. Our global total error estimate for the TOPEX/POSEIDON topography relative to the geoid for one 10-day period is found tio be 11 cm RMS. When the error in the geoid is removed, thereby providing an estimate of the time dependent error, the uncertainty in the topography is 3.5 cm root mean square (RMS). This level of accuracy is consistent with direct comparisons of TOPEX/POSEIDON altimeter heights with tide gauge measurements at 28 stations. In addition, the error correlation length scales are derived globally in both east-west and north-south directions, which should prove useful for data assimilation. The largest error correlation length scales are found in the tropics. Errors in the velocity field are smallest in midlatitude regions. For both variables the largest errors caused by uncertainty in the geoid. More accurate representations of the geoid await a dedicated geopotential satellite mission. Substantial improvements in the accuracy of ocean tide models are expected in the very near future from research with TOPEX/POSEIDON data.

Tsaoussi, Lucia S.↗