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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Feature Selection for Monitoring Erosive Cavitation on a Hydroturbine

This paper presents a method for comparing and evaluating cavitation detection features - the first step towards estimating remaining useful life (RUL) of hydroturbine runners that are impacted by erosive cavitation. The method can be used to quickly compare features created from cavitation survey data collected on any type of hydroturbine, sensor type, sensor location, and cavitation sensitivity parameter (CSP). Although manual evaluation and knowledge of hydroturbine cavitation is still required for our feature selection method, the use of principal component analysis greatly reduces the number of plots that require evaluation. We present a case study based on a cavitation survey data collected on a Francis hydroturbine located at a hydroelectric plant and demonstrate the selection of the most advantageous sensor type, sensor location, and CSP to use on this hydroturbine for long-term monitoring of erosive cavitation. Our method provides hydroturbine operators and researchers with a clear and effective means to determine preferred sensors, sensor placements, and CSPs while also laying the groundwork for determining RUL in the future.

13 HYDRO ENERGY↗
DOE OSTI2018

Estimates of climate system properties incorporating recent climate change

Abstract. Historical time series of surface temperature and ocean heat content changes are commonly used metrics to diagnose climate change and estimate properties of the climate system. We show that recent trends, namely the slowing of surface temperature rise at the beginning of the 21st century and the acceleration of heat stored in the deep ocean, have a substantial impact on these estimates. Using the Massachusetts Institute of Technology Earth System Model (MESM), we vary three model parameters that influence the behavior of the climate system: effective climate sensitivity (ECS), the effective ocean diffusivity of heat anomalies by all mixing processes (Kv), and the net anthropogenic aerosol forcing scaling factor. Each model run is compared to observed changes in decadal mean surface temperature anomalies and the trend in global mean ocean heat content change to derive a joint probability distribution function for the model parameters. Marginal distributions for individual parameters are found by integrating over the other two parameters. To investigate how the inclusion of recent temperature changes affects our estimates, we systematically include additional data by choosing periods that end in 1990, 2000, and 2010. We find that estimates of ECS increase in response to rising global surface temperatures when data beyond 1990 are included, but due to the slowdown of surface temperature rise in the early 21st century, estimates when using data up to 2000 are greater than when data up to 2010 are used. We also show that estimates of Kv increase in response to the acceleration of heat stored in the ocean as data beyond 1990 are included. Further, we highlight how including spatial patterns of surface temperature change modifies the estimates. We show that including latitudinal structure in the climate change signal impacts properties with spatial dependence, namely the aerosol forcing pattern, more than properties defined for the global mean, climate sensitivity, and ocean diffusivity.

Libardoni, Alex G.↗
DOE OSTI2021

Bias Characterization, Vertical Interpolation, and Horizontal Interpolation for Distributed Wind Siting Using Mesoscale Wind Resource Estimates

Much like their counterparts in utility-scale wind energy, developers of industrial, small-scale and distributed wind turbine deployments need to understand and accurately characterize the wind resource to properly assess the power generation and financial ramifications during siting and planning. National Renewable Energy Laboratory’s WIND (Wind Integration National Dataset) Toolkit (WTK) provides a best-in-class wind resource dataset generated using the Weather Research and Forecasting (WRF) model. This dataset includes parameters such as the wind speed, wind direction, and temperature at various heights, plus atmospheric stability near the surface. This data is available at 2-km spatial resolution and five-minute temporal resolution across 7 years, from 2007 to 2013 through a publicly accessible API interface. The Tools Assessing Performance (TAP) project seeks to extend this dataset to allow long term resource estimates and leverage it to better equip distributed wind equipment manufacturers, owner-operators, and installation professionals with better tools for practical siting applications. In this report, we present the results from our investigation within the TAP project focused on characterization of bias in WTK-based wind speed estimates and evaluation of vertical and horizontal interpolation techniques. We discuss the tradeoffs between different techniques and their combinations, as well as describe the lower bounds we determine for the studied validation errors. While the specific estimates we present are specific to WTK and the validation dataset we have chosen for this investigation (NREL's Wind Resource Meteorological Database), the overall analysis and the studied techniques are general enough to be applied to a broader set of wind datasets, both simulation-based and observational.

17 WIND ENERGY↗
DOE OSTI2022

The stellar parameters and elemental abundances from low-resolution spectra – I. 1.2 million giants from LAMOST DR8

As a typical data-driven method, deep learning becomes a natural choice for analysing astronomical data. In this study, we built a deep convolutional neural network (NN) to estimate basic stellar parameters $T\rm {_{eff}}$, log g , metallicity ([M/H] and [Fe/H]) and [α/M] along with nine individual elemental abundances ([C/Fe], [N/Fe], [O/Fe], [Mg/Fe], [Al/Fe], [Si/Fe], [Ca/Fe], [Mn/Fe], and [Ni/Fe]). The NN is trained using common stars between the APOGEE survey and the LAMOST survey. We used low-resolution spectra from LAMOST survey as input, and measurements from APOGEE as labels. For stellar spectra with the signal-to-noise ratio in g band larger than 10 in the test set, the mean absolute error (MAE) is 29 K for $T\rm {_{eff}}$, 0.07 dex for log g , 0.03 dex for both [Fe/H] and [M/H], and 0.02 dex for [α/M]. The MAE of most elements is between 0.02 and 0.04 dex. The trained NN was applied to 1210 145 giants, including sub-giants, from LAMOST DR8 within the range of stellar parameters 3500 K < $T\rm {_{eff}}$ < 5500 K, 0.0 dex < log g < 4.0 dex, −2.5 dex < [Fe/H] < 0.5 dex. The distribution of our results in the chemical spaces is highly consistent with APOGEE labels and stellar parameters show consistency with external high-resolution measurements from GALAH. The results in this study allow us to further studies based on LAMOST data and deepen our understanding of the accretion and evolution history of the Milky Way. The electronic version of the value added catalog is available at http://www.lamost.org/dr8/v1.1/doc/vac.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Tycho- Gaia Astrometric Solution Parallaxes and Proper Motions for Five Galactic Globular Clusters

We present a pilot study of Galactic globular cluster (GC) proper motion (PM) determinations using Gaia data. We search for GC stars in the Tycho- Gaia Astrometric Solution (TGAS) catalog from Gaia Data Release 1 (DR1), and identify five members of NGC 104 (47 Tucanae), one member of NGC 5272 (M3), five members of NGC 6121 (M4), seven members of NGC 6397, and two members of NGC 6656 (M22). By taking a weighted average of member stars, fully accounting for the correlations between parameters, we estimate the parallax (and, hence, distance) and PM of the GCs. This provides a homogeneous PM study of multiple GCs based on an astrometric catalog with small and well-controlled systematic errors and yields random PM errors similar to existing measurements. Detailed comparison to the available Hubble Space Telescope ( HST ) measurements generally shows excellent agreement, validating the astrometric quality of both TGAS and HST . By contrast, comparison to ground-based measurements shows that some of those must have systematic errors exceeding the random errors. Our parallax estimates have uncertainties an order of magnitude larger than previous studies, but nevertheless imply distances consistent with previous estimates. By combining our PM measurements with literature positions, distances, and radial velocities, we measure Galactocentric space motions for the clusters and find that these also agree well with previous analyses. Our analysis provides a framework for determining more accurate distances and PMs of Galactic GCs using future Gaia data releases. This will provide crucial constraints on the near end of the cosmic distance ladder and provide accurate GC orbital histories.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SU-G-TeP2-14: Universal Function Form for Photon Open Field In-Water Output Ratio

Purpose: In-Water-Output-Ratio (IWOR) plays a significant role in linac-based radiotherapy treatment planning, linking MUs to delivered radiation dose. For an open rectangular field, IWOR depends on both its width and length, and changes rapidly when one of them becomes small. In this study, a universal functional form is proposed to fit the open field IWOR tables in Varian TrueBeam representative datasets for all photon energies. Methods: A novel Generalized Mean formula is first used to estimate the Equivalent Square (ES) for a rectangular field. The formula’s weighting factor and power index are determined by collapsing all data points as much as possible onto a single curve in IWOR vs. ES plot. The result is then fitted with a novel universal function IWOR=1+b*Log(ES/10cm)/(ES/10cm)^c via a least-square procedure to determine the optimal values for parameters b and c. The maximum relative residual error in IWOR over the entire two-dimensional measurement table with field sizes between 3cm and 40cm is used to evaluate the quality of fit for the function. Results: The two-step fitting strategy works very well in determining the optimal parameter values for open field IWOR of each photon energies in the Varian data-set. Relative residual error ≤0.71% is achieved for all photon energies (including Flattening-Filter-Free modes) with field sizes between 3cm and 40cm. The optimal parameter values change smoothly with regular photon beam quality. Conclusion: The universal functional form fits the Varian TrueBeam open field IWOR measurement tables accurately with small relative residual errors for all photon energies. Therefore, it can be an excellent choice to represent IWOR in absolute dose and MU calculations. The functional form can also be used as a QA/commissioning tool to verify the measured data quality and consistency by checking the IWOR data behavior against the function for new photon energies with arbitrary beam quality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Comparison and Limitations of DVH-Based NTCP Models Derived From 3D-CRT and IMRT Data for Prediction of Gastrointestinal Toxicities in Prostate Cancer Patients by Using Propensity Score Matched Pair Analysis

Purpose: This study compared normal tissue complication probability (NTCP) modeling of chronic gastrointestinal toxicities following prostate cancer treatment for 2 treatment modalities. Possible factors causing discrepancies in optimal NTCP model parameters between 3-dimensional conformal radiation therapy (3D-CRT) and intensity modulated RT (IMRT) were analyzed and discussed, including the impact of patient characteristics, image guidance, toxicity scoring bias, and NTCP model limitations. Methods and Materials: Rectal wall dose-volume histograms of 1115 patients treated for prostate cancer under an adaptive radiation therapy protocol were used to model gastrointestinal toxicity grade ≥2 (according to Common Terminology Criteria for Adverse Events). A total of 457 patients were treated with 3D-CRT and 658 with IMRT. 3D-CRT patients were matched to IMRT patients based on various patient characteristics, using a propensity score–based algorithm. Parameters of the Lyman equivalent uniform dose and cut-off dose logistic regression NTCP models were estimated for the 2 matched treatment modalities and the combined group. Results: After they were matched, the 3D-CRT and IMRT groups contained 275 and 550 patients with a large discrepancy of 28.7% versus 7.8% toxicities, respectively (P<.001). For both NTCP models, optimal parameters found for the 3D-CRT groups did not fit the IMRT patients well and vice versa. Models developed for the combined data overestimated NTCP for the IMRT patients and underestimated NTCP for the 3D-CRT group. Conclusions: Our analysis did not reveal a single definitive cause for discrepancies of model parameters between 3D-CRT and IMRT. Patient characteristics and bias in toxicity scoring, as well as image guidance alone, are unlikely causes of the large discrepancy of toxicities. Whether the cause was inherent to the specific NTCP models used in this study needs to be verified by future investigations. Because IMRT is increasingly used clinically, it is important that appropriate NTCP model parameters are determined for this treatment modality.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Upper Limits on Gravitational Waves from Scorpius X-1 from a Model-based Cross-correlation Search in Advanced LIGO Data

We present the results of a semicoherent search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1, using data from the first Advanced LIGO observing run. The search method uses details of the modeled, parametrized continuous signal to combine coherently data separated by less than a specified coherence time, which can be adjusted to trade off sensitivity against computational cost. A search was conducted over the frequency range 25–2000 Hz, spanning the current observationally constrained range of binary orbital parameters. No significant detection candidates were found, and frequency-dependent upper limits were set using a combination of sensitivity estimates and simulated signal injections. The most stringent upper limit was set at 175 Hz, with comparable limits set across the most sensitive frequency range from 100 to 200 Hz. At this frequency, the 95% upper limit on the signal amplitude h {sub 0} is 2.3×10{sup −25} marginalized over the unknown inclination angle of the neutron star’s spin, and 8.0×10{sup −26} assuming the best orientation (which results in circularly polarized gravitational waves). These limits are a factor of 3–4 stronger than those set by other analyses of the same data, and a factor of ∼7 stronger than the best upper limits set using data from Initial LIGO science runs. In the vicinity of 100 Hz, the limits are a factor of between 1.2 and 3.5 above the predictions of the torque balance model, depending on the inclination angle; if the most likely inclination angle of 44° is assumed, they are within a factor of 1.7.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF). We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulation-based optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real- time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Nest survival modelling using a multi‐species approach in forests managed for timber and biofuel feedstock

Abstract Switchgrass ( Panicum virgatum ) intercropping is a novel forest management practice for biomass production intended to generate cellulosic feedstocks within intensively managed loblolly pine‐dominated landscapes. These pine plantations are important for early‐successional bird species, as short rotation times continually maintain early‐successional habitat. We tested the efficacy of using community models compared to individual surrogate species models in understanding influences on nest survival. We analysed nest data to test for differences in habitat use for 14 bird species in plots managed for switchgrass intercropping and controls within loblolly pine ( Pinus taeda ) plantations in Mississippi, USA. We adapted hierarchical models using hyper‐parameters to incorporate information from both common and rare species to understand community‐level nest survival. This approach incorporates rare species that are often discarded due to low sample sizes, but can inform community‐level demographic parameter estimates. We illustrate use of this approach in generating both species‐level and community‐wide estimates of daily survival rates for songbird nests. We were able to include rare species with low sample size (minimum n = 5) to inform a hyper‐prior, allowing us to estimate effects of covariates on daily survival at the community level, then compare this with a single‐species approach using surrogate species. Using single‐species models, we were unable to generate estimates below a sample size of 21 nests per species. Community model species‐level survival and parameter estimates were similar to those generated by five single‐species models, with improved precision in community model parameters. Covariates of nest placement indicated that switchgrass at the nest site (<4 m) reduced daily nest survival, although intercropping at the forest stand level increased daily nest survival. Synthesis and applications . Community models represent a viable method for estimating community nest survival rates and effects of covariates while incorporating limited data for rarely detected species. Intercropping switchgrass in loblolly pine plantations slightly increased daily nest survival at the research plot scale (0.1 km 2 ), although at a local scale (50 m 2 ) switchgrass negatively influenced nest survival. A likely explanation is intercropping shifted community composition, favouring species with greater disturbance tolerance.

Loman, Zachary G.↗
DOE OSTI2021

Estimating the color lifetime of energetic quarks

Using a simple geometric framework with a realistic nuclear density distribution, we fit published HERMES data to determine fundamental properties of hadronization using the nuclear medium as a spatial analyzer. Our approach uses a fit to the transverse momentum broadening observable and the hadronic multiplicity ratio; the simultaneous fit to two different observables strongly constrains the outcome. Using the known sizes of the target nuclei, we extract the color lifetime, finding a z h -dependent range of values from 2 to 8 fm/c for these data. We also extract estimates for the qˆ transport coefficient characterizing the strength of the interaction between the quark and the cold nuclear medium, finding an average value of 0.035±0.011 GeV 2 /fm. With a three-parameter model we obtain satisfactory fits to the data with a goodness-of-fit parameter χ 2 /dof of 1.1 or less. In a secondary fit of the results from that model we independently find a value for the Lund String Model string tension of 1.00±0.05 GeV/fm. We evaluated the sensitivity for extracting quark energy loss and effective in-medium hadronic cross sections using four-parameter variants of the model, finding large uncertainties in both cases. Our results suggest that hadronic interaction of forming hadrons in the nuclear medium is the primary dynamical cause of meson attenuation in the HERMES data, with quark energy loss playing a more minor role.

Electron-ion collider↗
DOE OSTI2019

Seismoacoustic Analysis of Chemical Explosions at the Nevada National Security Site

In recent years, two sets of chemical explosions have been conducted at the Nevada National Security Site (NNSS): 6 explosions from Phase I of the Source Physics Experiment (SPE) and 4 explosions from the Forensics Surface Events (FSE). We have estimated the explosive yield and depth-of-burial/height-of-burst of both from the synthesis of seismic and low-frequency acoustic data. Analysis of the seismic signal is performed using a waveform envelope technique. Using seismic data only, however, there is a tradeoff between the yield and depth or height of the explosion, which becomes especially acute for near-surface events. This tradeoff between yield and depth/height can be broken by including complementary analysis of the acoustic signal using the acoustic waveform inversion method. The acoustic signal also has a strong tradeoff between a yield and depth/height, but the slope is opposite that of the seismic for near-surface events. We use a likelihood function to combine the seismic and acoustic analysis and improve our estimates of yield and depth/height for these sets of explosions. The results are generally consistent with the known parameters, but can be improved upon by higher-frequency seismic analysis and improved acoustic impulse scaling for large scaled depths-of-burial. These events serve as prime examples of data fusion from multiple data streams. The challenge will be to apply the method to events at longer regional distances where the seismic signals will include phases traveling in the upper mantle, and the acoustic signals will be recorded as infrasound signals, which are subject to atmospheric variability.

58 GEOSCIENCES↗
DOE OSTI2017

An Estimate of Shallow, Low-Temperature Geothermal Resources of the United States

Low-temperature geothermal resources in the United States potentially hold an enormous quantity of thermal energy, useful for direct use in residential, commercial and industrial applications such as space and water heating, greenhouse warming, pool heating, aquaculture, and low-temperature manufacturing processes. Several studies published over the past 40 years have provided assessments of the resource potential for multiple types of low-temperature geothermal systems (e.g. hydrothermal convection, hydrothermal conduction, and enhanced geothermal systems) with varying temperature ranges and depths. This paper provides a summary and additional analysis of these assessments of shallow (= 3 km), low-temperature (30-150 degrees C) geothermal resources in the United States, suitable for use in direct-use applications. This analysis considers six types of geothermal systems, spanning both hydrothermal and enhanced geothermal systems (EGS). We outline the primary data sources and quantitative parameters used to describe resources in each of these categories, and present summary statistics of the total resources available. In sum, we find that low-temperature hydrothermal resources and EGS resources contain approximately 8 million and 800 million TWh of heat-in-place, respectively. In future work, these resource potential estimates will be used for modeling of the technical and market potential for direct-use geothermal applications for the U.S. Department of Energy's Geothermal Vision Study.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Dark Energy Survey Year 3 results: calibration of lens sample redshift distributions using clustering redshifts with BOSS/eBOSS

ABSTRACT We present clustering redshift measurements for Dark Energy Survey (DES) lens sample galaxies used in weak gravitational lensing and galaxy clustering studies. To perform these measurements, we cross-correlate with spectroscopic galaxies from the Baryon Acoustic Oscillation Survey (BOSS) and its extension, eBOSS. We validate our methodology in simulations, including a new technique to calibrate systematic errors that result from the galaxy clustering bias, and we find that our method is generally unbiased in calibrating the mean redshift. We apply our method to the data, and estimate the redshift distribution for 11 different photometrically selected bins. We find general agreement between clustering redshift and photometric redshift estimates, with differences on the inferred mean redshift found to be below |Δz| = 0.01 in most of the bins. We also test a method to calibrate a width parameter for redshift distributions, which we found necessary to use for some of our samples. Our typical uncertainties on the mean redshift ranged from 0.003 to 0.008, while our uncertainties on the width ranged from 4 to 9 per cent. We discuss how these results calibrate the photometric redshift distributions used in companion papers for DES Year 3 results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Petroleum Refinery Jobs and Economic Development Impact (JEDI) Model User Reference Guide

The Jobs and Economic Development Impact (JEDI) models, developed through the National Renewable Energy Laboratory (NREL), are user-friendly tools utilized to estimate the economic impacts at the local level of constructing and operating fuel and power generation projects for a range of conventional and renewable energy technologies. The JEDI Petroleum Refinery Model User Reference Guide was developed to assist users in employing and understanding the model. This guide provides information on the model's underlying methodology, as well as the parameters and references used to develop the cost data utilized in the model. This guide also provides basic instruction on model add-in features, operation of the model, and a discussion of how the results should be interpreted. Based on project-specific inputs from the user, the model estimates job creation, earning and output (total economic activity) for a given petroleum refinery. This includes the direct, indirect and induced economic impacts to the local economy associated with the refinery's construction and operation phases. Project cost and job data used in the model are derived from the most current cost estimations available. Local direct and indirect economic impacts are estimated using economic multipliers derived from IMPLAN software. By determining the regional economic impacts and job creation for a proposed refinery, the JEDI Petroleum Refinery model can be used to field questions about the added value refineries may bring to the local community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

An Efficient Bayesian Method for Advancing the Application of Deep Learning in Earth Science

Despite their wide and successful applications, deep learning (DL) models are prone to overfitting for small training datasets, produce a poor predictive performance for uncertain data, and provide point estimations without any indication of the accuracy and credibility. These limitations of the deterministic DL models hinder their effective application in Earth system science where the labelled data are sparse, noisy and incomplete with large uncertainty and where the predictive uncertainty quantification is needed for scientific understanding and policy decision making. Integration of Bayesian inference into DL models adds an estimate of uncertainty and regularization in the predictions. However, traditional Bayesian methods are computationally unaffordable and inflexible for high-dimensional problems, which limits their application to DL systems that typically have millions of model parameters. In this effort, we propose an efficient and general-purpose Bayesian inference method to advance DL model optimization and uncertainty quantification, so as to facilitate the adoption of DL in Earth sciences. In a demonstration, we integrate the proposed Bayesian method with a feedforward neural network (NN) to build a fast-to-evaluate surrogate of the complex Energy Exascale Earth System Land Model for efficient modeling. The formulated Bayesian NN, using a small number of training data, produces an accurate prediction with high credibility, whereas with the same small training size, the deterministic NN cannot yield a reasonable estimation and does not provide confidence information. The proposed Bayesian method is computationally efficient and flexible, capable of integration with diverse network variants such as convolutional NNs and recurrent NNs to advance the application of DL in Earth sciences.

Lu, Dan↗
DOE OSTI2023

Neural network-based single material beam-hardening correction for X-ray CT in Additive Manufacturing

Beam-hardening (BH) artifacts are ubiquitous in X-ray CT scans of dense metal additively manufactured (AM) parts. While linearization approaches are useful for correcting beam-hardened data from single material objects, they either require a calibration scan or detailed system and material composition information. In this paper, we introduce a neural network-based, material-agnostic method to correct beam-hardening artifacts. We train a neural network to map the acquired beam-hardened projection values and the corresponding estimated thickness of the part based on an initial segmentation to beam-hardening related parameters, which can be used to compute the coefficients of a linearizing correction polynomial. A key strength of our approach is that, once the network is trained, it can be used for correcting beam hardening from a variety of materials without any calibration scans or detailed system and material composition information. Furthermore, our method is robust to errors in the estimated thickness due to the typical challenge of obtaining an accurate initial segmentation from reconstructions impacted by BH artifacts. We demonstrate the utility of our method to obtain high-quality CT reconstructions from a collection of AM parts -- suppressing cupping and streaking artifacts

Rahman, Obaid↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗