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At least 721 records · Page 40

Density-Matrix Based Extended Lagrangian Born–Oppenheimer Molecular Dynamics

Extended Lagrangian Born–Oppenheimer molecular dynamics [ Phys. Rev. Lett. 2008, 100, 123004] is presented for Hartree–Fock theory, where the extended electronic degrees of freedom are represented by a density matrix, including fractional occupation numbers at elevated electronic temperatures. In contrast to regular direct Born–Oppenheimer molecular dynamics simulations, no iterative self-consistent field optimization is required prior to the force evaluations. To sample regions of the potential energy landscape where the gap is small or vanishing, which leads to particular convergence problems in regular direct Born–Oppenheimer molecular dynamics simulations, an adaptive integration scheme for the extended electronic degrees of freedom is presented. The integration scheme is based on a tunable, low-rank approximation of a fourth-order kernel, K, that determines the metric tensor, T ≡ K T K, used in the extended harmonic oscillator of the Lagrangian that generates the dynamics of the electronic degrees of freedom. Here, the formulation and algorithms provide a general guide to implement extended Lagrangian Born–Oppenheimer molecular dynamics for quantum chemistry, density functional theory, and semiempirical methods using a density matrix formalism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions

Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge.

97 MATHEMATICS AND COMPUTING↗

Simple imaging for the diamond anvil cell: Applications to hard-to-reach places

The employment of high-pressure gases as a pressure-transmitting medium, sample, or reactant for diamond anvil cell experiments is widespread. As a pressure transmitter, high-pressure gases are crucial to forming quasi-hydrostatic compression atmospheres for samples inside the uniaxially driven cell. We describe an optical design for forming high-resolution images of the gasket and sample chamber of the diamond anvil cell under high gas pressures in a gas loading apparatus. Our design is simple, is of low-cost, and may be easily adapted to suit gas loading apparatus of any design, as well as other common hard-to-reach environments in diamond anvil cell experiments, i.e., those with large stand-off distances, such as cryostats.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

AN EFFICIENT AUTOMATED VALIDATION PROCEDURE FOR EXOPLANET TRANSIT CANDIDATES

Surveys searching for transiting exoplanets have found many more candidates than they have been able to confirm as true planets. This situation is especially acute with the Kepler survey, which has found over 2300 candidates but has to date confirmed only a small fraction of them as planets. I present here a general procedure that can quickly be applied to any planet candidate to calculate its false positive probability. This procedure takes into account the period, depth, duration, and shape of the signal; the colors of the target star; arbitrary spectroscopic or imaging follow-up observations; and informed assumptions about the populations and distributions of field stars and multiple-star properties. Applying these methods to a sample of known Kepler planets, I demonstrate that many signals can be validated with very limited follow-up observations: in most cases with only a spectrum and an adaptive optics image. Additionally, I demonstrate that this procedure can reliably identify false positive signals. Because of the computational efficiency of this analysis, it is feasible to apply it to all Kepler planet candidates in the near future, and it will streamline the follow-up efforts for Kepler and other current and future transit surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

The Multiplicity of M Dwarfs in Young Moving Groups

We image 104 newly identified low-mass (mostly M-dwarf) pre-main sequence (PMS) members of nearby young moving groups (YMGs) with Magellan Adaptive Optics (MagAO) and identify 27 stellar binaries with instantaneous projected separation as small as 40 mas. Fifteen were previously unknown. The total number of multiple systems in this sample including spectroscopic and visual binaries from the literature is 36, giving a raw stellar multiplicity rate of at least 35{sub −4}{sup +5}% for this population. In the separation range of roughly 1–300 au in which infrared AO imaging is most sensitive, the raw multiplicity rate is at least 24{sub −4}{sup +5}% for binaries resolved by the MagAO infrared camera (Clio). The M-star subsample of 87 stars yields a raw multiplicity of at least 30{sub −4}{sup +5}% over all separations, 21{sub −4}{sup +5}% for secondary companions resolved by Clio from 1 to 300 au (23{sub −4}{sup +5}% for all known binaries in this separation range). A combined analysis with binaries discovered by the Search for Associations Containing Young stars shows that stellar multiplicity fraction as a function of mass over the range of 0.2 to 1.2 M {sub ☉} appears to be linearly flat, in contrast to the field, where multiplicity increases with mass. After bias corrections are applied, the multiplicity of low-mass YMG members (0.2–0.6 M {sub ☉}) is in excess of the field. The overall multiplicity fraction is also consistent with being constant in age and across YMGs, which suggests that multiplicity rates for this mass range are largely set by 10 Myr without appreciable evolution thereafter.

79 ASTRONOMY AND ASTROPHYSICS↗

Automatic Seismic Phase Picking Using Deep Learning for the EGS Collab project

Microseismic monitoring plays an important role in many energy-related and environmental industries.The microseismic event catalog and seismic structure of the subsurface are two of the primary outputsof the microseismic monitoring system. Though rough locations of microseismic events can be estimated automatically, obtaining high-resolution microseismic event locations requires a significant amount of human laborespecially on seismic phase picking. Unlike traditional automatic pickers that are usually less precise than human analysts, a fewrecently proposed algorithms based on deepneural networks(DNN)were able to match or surpass human performance for earthquake signals. Due to differences in the spatial scale of the study area, sensor sampling rate, and geometry of the monitoring system, it is not clear whether these deepneural networkmodels can be used to speed up microseismic data processing. In this paper, we adapted the DNN based technique for automatic phase picking of microseismic signals. We usedmicroseismic data recorded at the experiment 1 site of the enhancedgeothermal system (EGS) Collab project anddesigneda workflowthat we call transfer-learning aided double-difference tomography (TADT),thatcombines transfer learning and seismic tomography. We re-train an existing DNN with our data to obtain a newmodel using around 3500 seismogramsand associated manual phase picks. Thistransfer learnedmodel is able to reach human performance but muchfaster than human analysts. The transfer-learning-derived phase pickswereused to improve microseismic event locations and imagethesubsurface. The results are similar to or slightly better than those obtained with manual phase picks.

Chai, Chengping↗

Carbon Isotope Composition and the NDVI as Phenotyping Approaches for Drought Adaptation in Durum Wheat: Beyond Trait Selection

High-throughput phenotyping platforms provide valuable opportunities to investigate biomass and drought-adaptive traits. We explored the capacity of traits associated with drought adaptation such as aerial measurements of the Normalized Difference Vegetation Index (NDVI) and carbon isotope composition (δ13C) determined at the leaf level to predict genetic variation in biomass. A panel of 248 elite durum wheat accessions was grown at the Maricopa Phenotyping platform (US) under well-watered conditions until anthesis, and then irrigation was stopped and plot biomass was harvested about three weeks later. Globally, the δ13C values increased from the first to the second sampling date, in keeping with the imposition of progressive water stress. Additionally, δ13C was negatively correlated with final biomass, and the correlation increased at the second sampling, suggesting that accessions with lower water-use efficiency maintained better water status and, thus, performed better. Flowering time affected NDVI predictions of biomass, revealing the importance of developmental stage when measuring the NDVI and the effect that phenology has on its accuracy when monitoring genotypic adaptation to specific environments. The results indicate that in addition to choosing the optimal phenotypic traits, the time at which they are assessed, and avoiding a wide genotypic range in phenology is crucial.

54 ENVIRONMENTAL SCIENCES↗

Optimization for Peptide Sample Preparation for Urine Peptidomics

Analysis of native or endogenous peptides in biofluids can provide valuable insights into disease mechanisms. Furthermore, the detected peptides may also have utility as potential biomarkers for non-invasive monitoring of human diseases. The non-invasive nature of urine collection and the abundance of peptides in the urine makes analysis by high-throughput ‘peptidomics’ methods , an attractive approach for investigating the pathogenesis of renal disease. However, urine peptidomics methodologies can be problematic with regards to difficulties associated with sample preparation. The urine matrix can provide significant background interference in making the analytical measurements that it hampers both the identification of peptides and the depth of the peptidomics read when utilizing LC-MS based peptidome analysis. We report on a novel adaptation of the standard solid phase extraction (SPE) method to a modified SPE (mSPE) approach for improved peptide yield and analysis sensitivity with LC-MS based peptidomics in terms of time, cost, clogging of the LC-MS column, peptide yield, peptide quality, and number of peptides identified by each method. Expense and time requirements were comparable for both SPE and mSPE, but more interfering contaminants from the urine matrix were evident in the SPE preparations (e.g., clogging of the LC-MS columns, yellowish background coloration of prepared samples due to retained urobilin, lower peptide yields) when compared to the mSPE method. When we compared data from technical replicates of 4 runs, the mSPE method provided significantly improved efficiencies for the preparation of samples from urine (e.g., mSPE peptide identification 82% versus 18% with SPE; p = 8.92E-05). Additionally, peptide identifications, when applying the mSPE method, highlighted the biology of differential activation of urine peptidases during acute renal transplant rejection with distinct laddering of specific peptides, which was obscured for most proteins when utilizing the conventional SPE method. In conclusion, the mSPE method was found to be superior to the conventional, standard SPE method for urine peptide sample preparation when applying LC-MS peptidomics analysis due to the optimized sample clean up that provided improved experimental inference from the confidently identified peptides.

biomarker↗

Bayesian calibration of terrestrial ecosystem models: A study of advanced Markov chain Monte Carlo methods

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this study, a Differential Evolution Adaptive Metropolis (DREAM) algorithm was used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The DREAM is a multi-chain method and uses differential evolution technique for chain movement, allowing it to be efficiently applied to high-dimensional problems, and can reliably estimate heavy-tailed and multimodal distributions that are difficult for single-chain schemes using a Gaussian proposal distribution. The results were evaluated against the popular Adaptive Metropolis (AM) scheme. DREAM indicated that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identified one mode. The calibration of DREAM resulted in a better model fit and predictive performance compared to the AM. DREAM provides means for a good exploration of the posterior distributions of model parameters. Lastly, it reduces the risk of false convergence to a local optimum and potentially improves the predictive performance of the calibrated model.

54 ENVIRONMENTAL SCIENCES↗

On the Origin of Reverse Transcriptase-Using CRISPR-Cas Systems and Their Hyperdiverse, Enigmatic Spacer Repertoires

Cas1 integrase is the key enzyme of the clustered regularly interspaced short palindromic repeat (CRISPR)-Cas adaptation module that mediates acquisition of spacers derived from foreign DNA by CRISPR arrays. In diverse bacteria, the cas1 gene is fused (or adjacent) to a gene encoding a reverse transcriptase (RT) related to group II intron RTs. An RT-Cas1 fusion protein has been recently shown to enable acquisition of CRISPR spacers from RNA. Phylogenetic analysis of the CRISPR-associated RTs demonstrates monophyly of the RT-Cas1 fusion, and coevolution of the RT and Cas1 domains. Nearly all such RTs are present within type III CRISPR-Cas loci, but their phylogeny does not parallel the CRISPR-Cas type classification, indicating that RT-Cas1 is an autonomous functional module that is disseminated by horizontal gene transfer and can function with diverse type III systems. To compare the sequence pools sampled by RT-Cas1-associated and RT-lacking CRISPR-Cas systems, we obtained samples of a commercially grown cyanobacterium— Arthrospira platensis . Sequencing of the CRISPR arrays uncovered a highly diverse population of spacers. Spacer diversity was particularly striking for the RT-Cas1-containing type III-B system, where no saturation was evident even with millions of sequences analyzed. In contrast, analysis of the RT-lacking type III-D system yielded a highly diverse pool but reached a point where fewer novel spacers were recovered as sequencing depth was increased. Matches could be identified for a small fraction of the non-RT-Cas1-associated spacers, and for only a single RT-Cas1-associated spacer. Thus, the principal source(s) of the spacers, particularly the hypervariable spacer repertoire of the RT-associated arrays, remains unknown. IMPORTANCE While the majority of CRISPR-Cas immune systems adapt to foreign genetic elements by capturing segments of invasive DNA, some systems carry reverse transcriptases (RTs) that enable adaptation to RNA molecules. From analysis of available bacterial sequence data, we find evidence that RT-based RNA adaptation machinery has been able to join with CRISPR-Cas immune systems in many, diverse bacterial species. To investigate whether the abilities to adapt to DNA and RNA molecules are utilized for defense against distinct classes of invaders in nature, we sequenced CRISPR arrays from samples of commercial-scale open-air cultures of Arthrospira platensis , a cyanobacterium that contains both RT-lacking and RT-containing CRISPR-Cas systems. We uncovered a diverse pool of naturally occurring immune memories, with the RT-lacking locus acquiring a number of segments matching known viral or bacterial genes, while the RT-containing locus has acquired spacers from a distinct sequence pool for which the source remains enigmatic. While the majority of CRISPR-Cas immune systems adapt to foreign genetic elements by capturing segments of invasive DNA, some systems carry reverse transcriptases (RTs) that enable adaptation to RNA molecules. From analysis of available bacterial sequence data, we find evidence that RT-based RNA adaptation machinery has been able to join with CRISPR-Cas immune systems in many, diverse bacterial species. To investigate whether the abilities to adapt to DNA and RNA molecules are utilized for defense against distinct classes of invaders in nature, we sequenced CRISPR arrays from samples of commercial-scale open-air cultures of Arthrospira platensis , a cyanobacterium that contains both RT-lacking and RT-containing CRISPR-Cas systems. We uncovered a diverse pool of naturally occurring immune memories, with the RT-lacking locus acquiring a number of segments matching known viral or bacterial genes, while the RT-containing locus has acquired spacers from a distinct sequence pool for which the source remains enigmatic.

horizontal gene transfer↗

Adapting ecological risk valuation for natural resource damage assessment in water pollution

Highlights: • An approach is proposed to assess water damages from pollution incident. • Sewage treatment cost is analyzed to estimate the value of water quality damage. • DEA is employed to classify samples of sewage plants based on their efficiency. • The relation between treatment cost and the decrease of COCs is determined. • The range of damage cost is determined based on efficient and inefficient samples. Ecological risk assessment can address requirements of natural resource damage assessment by quantifying the magnitude of possible damages to the ecosystem. This paper investigates an approach to assess water damages from pollution incident on the basis of concentrations of contaminants. The baseline of water pollution is determined with not-to-exceed concentration of contaminants required by water quality standards. The values of damage cost to water quality are estimated through sewage treatment cost. To get a reliable estimate of treatment cost, DEA is employed to classify samples of sewage plants based on their efficiency of sewage treatment. And exponential fitting is adopted to determine the relation between treatment cost and the decrease of COCs. The range of damage costs is determined through the fitting curves respectively based on efficient and inefficient samples.

54 ENVIRONMENTAL SCIENCES↗

Herbivorous Fish Microbiome Adaptations to Sulfated Dietary Polysaccharides

Marine herbivorous fish that feed primarily on macroalgae, such as those from the genus Kyphosus, are essential for maintaining coral health and abundance on tropical reefs. Here, deep metagenomic sequencing and assembly of gut compartment-specific samples from three sympatric, macroalgivorous Hawaiian kyphosid species have been used to connect host gut microbial taxa with predicted protein functional capacities likely to contribute to efficient macroalgal digestion. Bacterial community compositions, algal dietary sources, and predicted enzyme functionalities were analyzed in parallel for 16 metagenomes spanning the mid- and hindgut digestive regions of wild-caught fishes. Gene colocalization patterns of expanded carbohydrate (CAZy) and sulfatase (SulfAtlas) digestive enzyme families on assembled contigs were used to identify likely polysaccharide utilization locus associations and to visualize potential cooperative networks of extracellularly exported proteins targeting complex sulfated polysaccharides. These insights into the gut microbiota of herbivorous marine fish and their functional capabilities improve our understanding of the enzymes and microorganisms involved in digesting complex macroalgal sulfated polysaccharides.

09 BIOMASS FUELS↗

Sub-pixel high-resolution imaging of high-energy x-rays inspired by sub-wavelength optical imaging

We have developed and demonstrated an image super-resolution method—XR-UNLOC: X-Ray UNsupervised particle LOCalization—for hard x-rays measured with fast-frame-rate detectors that is an adaptation of the principle of photo-activated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), which enabled biological fluorescence imaging at sub-optical-wavelength scales. We demonstrate the approach on experimental coherent Bragg diffraction data measured with 52 keV x-rays from a nanocrystalline sample. From this sample, we resolve the fine fringe detail of a high-energy x-ray Bragg coherent diffraction pattern to an upsampling factor of 16 of the native pixel pitch of 30 μ m of a charge-integrating fastCCD detector. This was accomplished by analysis of individual photon locations in a series of “nearly-dark” instances of the diffraction pattern that each contain only a handful of photons. Central to our approach was the adaptation of the UNLOC photon fitting routine for PALM/STORM to the hard x-ray regime to handle much smaller point spread functions, which required a different statistical test for photon detection and for sub-pixel localization. A comparison to a photon-localization strategy used in the x-ray community (“droplet analysis”) showed that XR-UNLOC provides significant improvement in super-resolution. We also developed a metric by which to estimate the limit of reliable upsampling with XR-UNLOC under a given set of experimental conditions in terms of the signal-to-noise ratio of a photon detection event and the size of the point spread function for guiding future x-ray experiments in many disciplines where detector pixelation limits must be overcome.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Cross-validation based single response adaptive design of experiments for Kriging metamodeling of deterministic computer simulations

A new approach for single response adaptive design of deterministic computer experiments is presented. The approach is called SFCVT, for Space-Filling CrossValidation Tradeoff. SFCVT uses metamodeling to obtain an estimate of cross-validation errors, which are maximized subject to a constraint on space filling to determine sample points in the design space. The proposed method is compared, using a test suite of forty four numerical examples, with three DOE methods from the literature. The numerical test examples can be classified into symmetric and asymmetric functions. Symmetric examples refer to functions for which the extreme points are located symmetrically in the design space and asymmetric examples are those for which the extreme regions are not located in a symmetric fashion in the design space. Based upon the comparison results for the numerical examples, it is shown that SFCVT performs better than an existing adaptive and a non-adaptive DOE method for asymmetric multimodal functions with high nonlinearity near the boundary, and is comparable for symmetric multimodal functions and other test problems. The proposed approach is integrated with a multi-scale heat exchanger optimization tool to reduce the computational effort involved in the design of novel air-to-water heat exchangers. In conclusion, the resulting designs are shown to be significantly more compact than mainstream heat exchanger designs.

97 MATHEMATICS AND COMPUTING↗

Development and characterization of a wild emmer wheat backcross introgression population for hard winter wheat improvement

Abstract Wild emmer wheat (Triticum turgidumsubsp.dicoccoides) is the tetraploid progenitor of hexaploid bread wheat (Triticum aestivumL.) and is known to be a valuable source of genetic variation for wheat improvement. However, direct evaluation of wild emmer diversity for agronomic potential has limited value unless performed in the backgrounds of adapted cultivars. Here, we present a genetic characterization of a population of 1601 backcross recombinant inbred lines, with an average genome composition of 75% bread wheat and 25% wild emmer. Low‐coverage whole‐genome sequencing allowed introgressions and aneuploidies to be identified at a relatively low cost per sample. We identified a relatively large proportion of small introgressions (median length 38 Mb), and we found introgressions to be distributed across all chromosomes. Approximately 44% of genotyped progeny carried at least one aneuploidy, with monosomies being by far the most common. This population, which we have denoted as the Great Plains Wild Emmer/Hard Winter Wheat introgression population (GPWEW‐IP), is, to our knowledge, the first introgression population developed through the direct hybridization of wild emmer wheat and US‐adapted hard winter wheat. We believe that this population represents a valuable resource for wheat breeders and will accelerate the discovery and integration of useful variation from wild emmer wheat.

Genetics & Heredity↗

High-throughput, dynamic, multi-dimensional: an expanding repertoire of plant respiration measurements

A recent burst of technological innovation and adaptation has greatly improved our ability to capture respiration rate data from plant sources. At the tissue level, several independent respiration measurement options are now available, each with distinct advantages and suitability, including high-throughput sampling capacity. These advancements facilitate the inclusion of respiration rate data into large-scale biological studies such as genetic screens, ecological surveys, crop breeding trials, and multi-omics molecular studies. As a result, our understanding of the correlations of respiration with other biological and biochemical measurements is rapidly increasing. Difficult questions persist concerning the interpretation and utilization of respiration data; concepts such as allocation of respiration to growth versus maintenance, the unnecessary or inefficient use of carbon and energy by respiration, and predictions of future respiration rates in response to environmental change are all insufficiently grounded in empirical data. However, we emphasize that new experimental designs involving novel combinations of respiration rate data with other measurements will flesh-out our current theories of respiration. Furthermore, dynamic recordings of respiration rate, which have long been used at the scale of mitochondria, are increasingly being used at larger scales of size and time to reflect processes of cellular signal transduction and physiological response to the environment. Herein, we also highlight how respiratory methods are being better adapted to different plant tissues including roots and seeds, which have been somewhat neglected historically.

59 BASIC BIOLOGICAL SCIENCES↗

Use of anti-CRISPR protein AcrIIA4 as a capture ligand for CRISPR/Cas9 detection

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) ribonucleoprotein (RNP) complex is an RNA-guided DNA-nuclease that is part of the bacterial adaptive immune system. CRISPR/Cas9 RNP has been adapted for targeted genome editing within cells and whole organisms with new applications vastly outpacing detection and quantification of gene-editing reagents. Detection of the CRISPR/Cas9 RNP within biological samples is critical for assessing gene-editing reagent delivery efficiency, retention, persistence, and distribution within living organisms. Conventional detection methods are effective, yet the expense and lack of scalability for antibody-based affinity reagents limit these techniques for clinical and/or field settings. This necessitates the development of low cost, scalable CRISPR/Cas9 RNP affinity reagents as alternatives or augments to antibodies. Herein, we report the development of the Streptococcus pyogenes anti-CRISPR/Cas9 protein, AcrIIA4, as a novel affinity reagent. An engineered cysteine linker enables covalent immobilization of AcrIIA4 onto glassy carbon electrodes functionalized via aryl diazonium chemistry for detection of CRISPR/Cas9 RNP by electrochemical, fluorescent, and colorimetric methods. Electrochemical measurements achieve a detection of 280 pM RNP in reaction buffer and 8 nM RNP in biologically representative conditions. Our results demonstrate the ability of anti-CRISPR proteins to serve as robust, specific, flexible, and economical recognition elements in biosensing/quantification devices for CRISPR/Cas9 RNP.

59 BASIC BIOLOGICAL SCIENCES↗

Multifidelity uncertainty quantification with models based on dissimilar parameters

Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to significantly reduce the variance of statistical estimators while preserving the bias of the highest-fidelity model, provided that the low-fidelity models are well correlated. However, maintaining a high level of correlation can be challenging, especially when models depend on different input uncertain parameters, which drastically reduces the correlation. Existing MF UQ approaches do not adequately address this issue. In this work, we propose a new sampling strategy that exploits a shared space to improve the correlation among models with dissimilar parameterization. We achieve this by transforming the original coordinates onto an auxiliary manifold using the adaptive basis (AB) method (Tipireddy and Ghanem, 2014). The AB method has two main benefits: (1) it provides an effective tool to identify the low-dimensional manifold on which each model can be represented, and (2) it enables easy transformation of polynomial chaos representations from high- to low-dimensional spaces. This latter feature is used to identify a shared manifold among models without requiring additional evaluations. Here we present two algorithmic flavors of the new estimator to cover different analysis scenarios, including those with legacy and non-legacy high-fidelity (HF) data. We provide numerical results for analytical examples, a direct field acoustic test, and a finite element model of a nuclear fuel assembly. For all examples, we compare the proposed strategy against both single-fidelity and MF estimators based on the original model parameterization.

42 ENGINEERING↗