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At least 649 records · Page 36

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DOE OSTI2020

Collaborative Exploration of Scientific Datasets Using Immersive and Statistical Visualization: Preprint

We discuss the value of collaborative, immersive visualization for the exploration of scientific datasets and review techniques and tools that have been developed and deployed at the National Renewable Energy Laboratory (NREL). We believe that collaborative visualizations linking statistical interfaces and graphics on laptops and high-performance computing (HPC) with 3D visualizations on immersive displays (head-mounted displays and large-scale immersive environments) enable scientific workflows that further rapid exploration of large, high-dimensional datasets by teams of analysts. We present a framework, PlottyVR, that blends statistical tools, general-purpose programming environments, and simulation with 3D visualizations. To contextualize this framework, we propose a categorization and loose taxonomy of collaborative visualization and analysis techniques. Finally, we describe how scientists and engineers have adopted this framework to investigate large, complex datasets.

collaborative visualization↗

DOE OSTI2021

Collaborative Exploration of Scientific Datasets Using Immersive and Statistical Visualization

collaborative visualization↗

DOE OSTI2015

Why Use Geo-statistics For Nuclear Decommissioning Projects? - 15567

Before nuclear facilities are dismantled, and contaminated sites remediated, their initial radiological state must be characterized. Combining historical information, non-destructive measurements and lab analysis results, the statistical method of geo-statistics allows all available data for 2D or 3D contamination mapping to be integrated and enhanced, and helps quantify estimation uncertainties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Transferability in the future climate of a statistical downscaling method for precipitation in France

A statistical downscaling approach for precipitation in France based on the analog method and its evaluation for different combinations of predictors is described, with a focus on the transferability of the method to the future climate. First, the realism of downscaled present-day precipitation climatology and inter-annual variability for different combinations of predictors from four reanalyses is assessed. Satisfactory results are obtained but elaborated predictors do not lead to major and consistent across reanalyses improvements. The downscaling method is then evaluated on its capacity to capture precipitation trends in the last decades. As uncertainties in downscaled trends due to the choice of the reanalysis are large andobserved trends are weak, this analysis does not lead to strong conclusions on the applicability of the method to a changing climate. The temporal transferability is then assessed thanks to a perfect-model framework. The statistical downscaling relationship is built using present-day predictors and precipitation simulated by twelve regional climate models. The entire projections are then downscaled, and future downscaled and simulated precipitation changes are compared. A good temporal transferability is obtained only with a specific combination of predictors. Finally, the regional climate models are downscaled thanks to the relationship built with reanalyses and observations, for the best combination of predictors. Results are similar to the changes simulated by the models, which reinforces our confidence in the realism of the models and of the downscaling method. Uncertainties in precipitation change due to reanalyses are found to be limited compared to those due to regional simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

On the Statistical Calibration of Physical Models

ABSTRACT We introduce a novel statistical calibration framework for physical models, relying on probabilistic embedding of model discrepancy error within the model. For clarity of illustration, we take the measurement errors out of consideration, calibrating a chemical model of interest with respect to a more detailed model, considered as “truth” for the present purpose. We employ Bayesian statistical methods for such model‐to‐model calibration and demonstrate their capabilities on simple synthetic models, leading to a well‐defined parameter estimation problem that employs approximate Bayesian computation. The method is then demonstrated on two case studies for calibration of kinetic rate parameters for methane air chemistry, where ignition time information from a detailed elementary‐step kinetic model is used to estimate rate coefficients of a simple chemical mechanism. We show that the calibrated model predictions fit the data and that uncertainty in these predictions is consistent in a mean‐square sense with the discrepancy from the detailed model data.

Sargsyan, K.↗

DOE OSTI2017

Comparing identified and statistically significant lipids and polar metabolites in 15-year old serum and dried blood spot samples for longitudinal studies: Comparing lipids and metabolites in serum and DBS samples

The use of dried blood spots (DBS) has many advantages over traditional plasma and serum samples such as smaller blood volume required, storage at room temperature, and ability for sampling in remote locations. However, understanding the robustness of different analytes in DBS samples is essential, especially in older samples collected for longitudinal studies. Here we analyzed DBS samples collected in 2000-2001 and stored at room temperature and compared them to matched serum samples stored at -80°C to determine if they could be effectively used as specific time points in a longitudinal study following metabolic disease. Four hundred small molecules were identified in both the serum and DBS samples using gas chromatograph-mass spectrometry (GC-MS), liquid chromatography-MS (LC-MS) and LC-ion mobility spectrometry-MS (LC-IMS-MS). The identified polar metabolites overlapped well between the sample types, though only one statistically significant polar metabolite in a case-control study was conserved, indicating degradation occurs in the DBS samples affecting quantitation. Differences in the lipid identifications indicated that some oxidation occurs in the DBS samples. However, thirty-six statistically significant lipids correlated in both sample types indicating that lipid quantitation was more stable across the sample types.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Wormholes from heavy operator statistics in AdS/CFT

We construct higher dimensional Euclidean AdS wormhole solutions that reproduce the statistical description of the correlation functions of an ensemble of heavy CFT operators. We consider an operator which effectively backreacts on the geometry in the form of a thin shell of dust particles. Assuming dynamical chaos in the form of the ETH ansatz, we demonstrate that the semiclassical path integral provides an effective statistical description of the microscopic features of the thin shell operator in the CFT. The Euclidean wormhole solutions provide microcanonical saddlepoint contributions to the cumulants of the correlation functions over the ensemble of operators. We finally elaborate on the role of these wormholes in the context of non-perturbative violations of bulk global symmetries in AdS/CFT.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Evaluation of precipitation indices in suites of dynamically and statistically downscaled regional climate models over Florida

Abstract The present work evaluates historical precipitation and its indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in suites of dynamically and statistically downscaled regional climate models (RCMs) against NOAA’s Global Historical Climatology Network Daily (GHCN-Daily) dataset over Florida. The models examined here are: (1) nested RCMs involved in the North American CORDEX (NA-CORDEX) program, (2) variable resolution Community Earth System Models (VR-CESM), (3) Coupled Model Intercomparison Project phase 5 (CMIP5) models statistically downscaled using localized constructed analogs (LOCA) technique. To quantify observational uncertainty, three in situ-based (PRISM, Livneh, CPC) and three reanalysis (ERA5, MERRA2, NARR) datasets are also evaluated against the station data. The reanalyses and dynamically downscaled RCMs generally underestimate the magnitude of the monthly precipitation and the frequency of the extreme rainfall in summer. The models forced with CanESM2 miss the phase of the seasonality of extreme precipitation. All models and reanalyses severely underestimate both the mean and interannual variability of mean wet-day precipitation (SDII), consecutive dry days (CDD), and overestimate consecutive wet days (CWD). Metric analysis suggests large uncertainty across NA-CORDEX models. Both the LOCA and VR-CESM models perform better than the majority of models. Overall, RegCM4 and WRF models perform poorer than the median model performance. The performance uncertainty across models is comparable to that in the reanalyses. Specifically, NARR performs poorer than the median model performance in simulating the mean indices and MERRA2 performs worse than the majority of models in capturing the interannual variability of the indices.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets

This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables corresponding to independent physical factors. Introducing Aux-VAE, a novel architecture within the classical Variational Autoencoder framework, we achieve disentanglement with minimal modifications to the standard VAE loss function by leveraging prior statistical knowledge through auxiliary variables. These variables guide the shaping of the latent space by aligning latent factors with learned auxiliary variables. We validate the efficacy of Aux-VAE through comparative assessments on multiple datasets, including astronomical simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Reduction of amplified spontaneous emission noise using a statistical signal adaptive filter

This paper proposes a statistical signal adaptive filter (SSAF) based on adaptive total variation to reduce amplified spontaneous emission (ASE) noise in optical amplifiers, e.g. erbium doped fiber amplifiers. Non-return-to-zero on–off keying signal to noise ratio (SNR) is degraded by ASE noise. However, this can be improved by squeezing ASE using the proposed SSAF after photodetection to effectively remove the noise by analyzing the signal movement statistically. We obtained 4-dB SNR gain by reducing ASE beat noise compared with not applying SSAF. Simultaneous optical bandpass filter and SSAF application reduced the 2-dB power penalty for a 20-km optical link more than when only SSAF was used.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A statistical and simulation-informed model for estimating permeability from pore size distribution in saturated geomaterials

Accurate permeability estimation is essential across subsurface engineering applications but remains challenging due to the complex pore structures of natural geomaterials. Traditional empirical methods and simplified theoretical models often inadequately capture the role of pore size distribution and connectivity. Here, this study develops a statistical and simulation-informed permeability model that collapses pore-scale complexity into a compact scaling of the form k = αϕμ d 2 , where ϕ is porosity, μ d is mean pore size, and α is a weakly varying coefficient. By combining pore network simulations with statistical analysis of unimodal and bimodal pore size distributions, we identify three key findings: (i) permeability is much more sensitive to mean pore size than to porosity; (ii) across extensive datasets, the ratio σ d /μ d (standard deviation to mean) clusters around a characteristic value ∼0.4, allowing the effects of the full pore size distribution to be represented by μ d and a narrowly varying α ≈ 0.05; and (iii) for bimodal systems, there exists a critical fraction of small pores ∼0.78 above which flow becomes small-pore dominated, enabling the definition of an effective flow-controlling pore population and facilitating simplified permeability estimation for such systems. The resulting model, which requires only porosity and a representative mean pore size as inputs, is validated against comprehensive experimental datasets (>1700 samples) spanning diverse soils and rocks and achieves good predictive accuracy. Overall, this work provides a physically grounded yet practically simple permeability estimator suitable for subsurface engineering, environmental protection, and resource management applications.

Permeability↗

DOE OSTI2017

Braiding statistics and link invariants of bosonic/fermionic topological quantum matter in 2+1 and 3+1 dimensions

Topological Quantum Field Theories (TQFTs) pertinent to some emergent low energy phenomena of condensed matter lattice models in 2+1 and 3+1 dimensions are explored. Many of our TQFTs are highly-interacting without free quadratic analogs. Some of our bosonic TQFTs can be regarded as the continuum field theory formulation of Dijkgraaf–Witten twisted discrete gauge theories. Other bosonic TQFTs beyond the Dijkgraaf–Witten description and all fermionic TQFTs (namely the spin TQFTs) are either higher-form gauge theories where particles must have strings attached, or fermionic discrete gauge theories obtained by gauging the fermionic Symmetry-Protected Topological states (SPTs). We analytically calculate both the Abelian and non-Abelian braiding statistics data of anyonic particle and string excitations in these theories, where the statistics data can one-to-one characterize the underlying topological orders of TQFTs. Namely, we derive path integral expectation values of links formed by line and surface operators in these TQFTs. The acquired link invariants include not only the familiar Aharonov–Bohm linking number, but also Milnor triple linking number in 3 dimensions, triple and quadruple linking numbers of surfaces, and intersection number of surfaces in 4 dimensions. We also construct new spin TQFTs with the corresponding knot/link invariants of Arf(–Brown–Kervaire), Sato–Levine and others. We propose a new relation between the fermionic SPT partition function and the Rokhlin invariant. As an example, we can use these invariants and other physical observables, including ground state degeneracy, reduced modular $S^{xy}$ and $T^{xy}$ matrices, and the partition function on $RP^3$ manifold, to identify all $\nu \in Z_8$ classes of 2+1 dimensional gauged $Z_2$-Ising-symmetric $Z^f_2$-fermionic Topological Superconductors (realized by stacking v layers of a pair of chiral and anti-chiral -wave superconductors [ p + ip and p - ip ], where boundary supports non-chiral Majorana–Weyl modes) with continuum spin-TQFTs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

A statistical analysis on transcriptome sequences: The enrichment of Alu-element is associated with subcellular location

Highlights: • The enrichment of Alu-element is associated with subcellular location. • mRNAs and lncRNAs share the same sequence form for Alu-element. • Alu-element is preferentially located at the 3′ end. The Alu-element plays important roles in mediating alternative splicing, RNA editing and translation regulation. However, the distribution and function of the Alu-element are never analysed at the transcriptome level. This study presents a statistical analysis of the Alu-element on human transcriptome. We found that mRNAs and lncRNAs share the same sequence form for the Alu-element. The Alu-element covers 5.8% of the coding transcripts and 17.1% of the coding genes for mRNAs, and covers 9.3% of the transcripts and 13.6% of the genes for lncRNAs. The Alu-element is preferentially located at the 3′ end. Statistical analysis demonstrates that the enrichment of Alu-element is associated with subcellular location. For instance, Alu-inclusive transcripts are overexpressed in nucleus, mitochondrion and Golgi apparatus membrane while under-expressed in cell membrane and extracellular space. We found that genes contain both Alu−element and S− domains of 7SL RNA are all associated with cellular activities carried out in mitochondrion.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Statistical distributions for transient transport

Here, this paper introduces the use of statistical distributions based on transport differential equations for clear distinction of transport modes within transient kinetic experiments. More specifically, novel techniques are developed for the transient data obtained through the Temporal Analysis of Products (TAP) reactor and are applicable to experiments where pulse response into a gas flow is used. The methodology allows distinguishing between two domains of diffusion transport in heterogeneous catalytic systems, i.e., Knudsen and non-Knudsen diffusion, using statistical fingerprints, and finding the transition domain. Two distribution parameters were obtained that directly result in coefficients that correspond to the concentration and the rate of transport. Using a linear relationship between the rate and concentration coefficients, Knudsen diffusion is revealed when the rate of transport is constant and non-Knudsen diffusion is confirmed when the rate of transport coefficient is a function of the concentration coefficient. As a result, accurate transport information can be extracted from experimental data even in the presence of comprising instrument drift or noise particularly when analyzing higher pressure pulse responses with complex transport. This enables more direct investigation of experiments influenced by gas-phase reactions.

TAP reactor↗

DOE OSTI2017

Comparing multiple statistical methods for inverse prediction in nuclear forensics applications

Forensic science seeks to predict source characteristics using measured observables. Statistically, this objective can be thought of as an inverse problem where interest is in the unknown source characteristics or factors ( X ) of some underlying causal model producing the observables or responses (Y = g ( X ) + error ). Here, this paper reviews several statistical methods for use in inverse problems and demonstrates that comparing results from multiple methods can be used to assess predictive capability. Motivation for assessing inverse predictions comes from the desired application to historical and future experiments involving nuclear material production for forensics research in which inverse predictions, along with an assessment of predictive capability, are desired.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

A statistical model to predict ignition probability

Ignition capability is a critical design constraint for aeronautical gas turbines. However the current trend toward overall lean burn is detrimental to the engine ignition and relight and the ignition system must be adapted to ensure a fast and reliable light-round in all circumstances. As ignition is a stochastic phenomenon, the optimization of an ignition system requires to build ignition probability maps, which is difficult and costly with either experiment or numerical simulation as both require many tests. Here, this work proposes a model to predict the ignition probability map, knowing only flow statistics in non-reacting conditions, i.e., with only one test. The originality of the model is to construct statistics of the flame kernel trajectory, which are then combined with local flow indicators to evaluate the ignition probability at the considered sparking location. Application to a swirled burner operated in premixed, non-premixed and spray combustion modes illustrates the model concepts and demonstrates its ability to recover the experimental ignition map with good accuracy.

42 ENGINEERING↗

DOE OSTI2024

Spatially-resolved soot evolution and statistics in high-pressure diesel spray flames using two-color pyrometry

Previous studies of diesel spray flames have focused on details of the sooting behavior mainly during the quasi-steady period, but few have considered the spatially-resolved transient evolution in combination with injection-to-injection variations. In this study, a 500-injection data set is utilized to investigate the temporal evolution of a spray flame during auto-ignition, the premixed burn phase, and the quasi-steady period. Spatially-resolved OH* chemiluminescence data provide ignition delay times and reaction zone locations. Two-color pyrom-etry with a vastly improved optical system is used to quantify spatially-resolved soot evolution and its statistical variations. The ambient thermodynamic con-ditions are slightly below those in modern diesel engines, resulting in longer lift-off lengths and lower overall soot production. Spatially, soot formation in the lift-off region is small, but appreciable soot forms in the jet core and jet head regions, while it oxidizes gradually on the jet periphery. Total soot mass profiles indicate that regions with larger local soot mass take longer to form, but are the first to oxidize. Probability distributions of soot mass in localized regions indicate that a few injections with high soot mass bias the average soot mass data towards higher values. Altogether, results show good agreement with previous studies employing different diagnostic techniques while providing statistical details of transient and localized soot behavior in high pressure diesel spray flames in support of the related modeling efforts.

09 BIOMASS FUELS↗

DOE OSTI2023

Challenges for the statistical gravitational-wave method to measure the Hubble constant

Gravitational waves (GW) can be employed as standard sirens that will soon measure the Hubble constant with sufficient precision to weigh in on the ~5σ Hubble tension. Most GW sources will have no identified electromagnetic counterpart, leading to uncertainty in the redshift of the source, and in turn a degeneracy between host galaxy distance, redshift, and H 0 . In the case where no electromagnetic counterparts are identified, it has been proposed that a statistical canvassing of candidate GW hosts, found in a large galaxy survey for example, can be used to accurately constrain the Hubble constant. We study and simulate this "galaxy voting" method to compute H 0 . Here, we find that the Hubble constant posterior is in general biased relative to the true value even when making optimistic assumptions about the statistical properties of the sample. Using the MICECAT light-cone catalog, we find that the bias in the H0 posteriors depends on the realization of the underlying galaxy sample and the precision of the GW source distance measurement.

79 ASTRONOMY AND ASTROPHYSICS↗