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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.

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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Equation Discovery Using Fast Function Extraction: a Deterministic Symbolic Regression Approach

Advances in machine learning (ML) coupled with increased computational power have enabled identification of patterns in data extracted from complex systems. ML algorithms are actively being sought in recovering physical models or mathematical equations from data. This is a highly valuable technique where models cannot be built using physical reasoning alone. In this paper, we investigate the application of fast function extraction (FFX), a fast, scalable, deterministic symbolic regression algorithm to recover partial differential equations (PDEs). FFX identifies active bases among a huge set of candidate basis functions and their corresponding coefficients from recorded snapshot data. This approach uses a sparsity-promoting technique from compressive sensing and sparse optimization called pathwise regularized learning to perform feature selection and parameter estimation. Furthermore, it recovers several models of varying complexity (number of basis terms). FFX finally filters out many identified models using non-dominated sorting and forms a Pareto front consisting of optimal models with respect to minimizing complexity and test accuracy. Numerical experiments are carried out to recover several ubiquitous PDEs such as wave and heat equations among linear PDEs and Burgers, Korteweg–de Vries (KdV), and Kawahara equations among higher-order nonlinear PDEs. Additional simulations are conducted on the same PDEs under noisy conditions to test the robustness of the proposed approach.

42 ENGINEERING↗
DOE OSTI2017

Optimizing the Performance of Sparse-Matrix Vector Products on Next-Generation Processors

Matrix-vector products are ubiquitous in high-performance scientific applications and have a growing set of occurrences in advanced data analysis activities. Achieving high performance for these kernels is therefore paramount, in part, because these operations can consume vast amounts of application execution time. In this report we document the development of several sparse-matrix vector product kernel implementations using a variety of programming models and approaches. Each kernel is run on a broad set of matrices selected to demonstrate the wide variety of matrix structure and sparsity that is possible with a single, generic kernel. For benchmarking and performance analysis, we utilize leading computing architectures for the NNSA/ASC program including Intel's Knights Landing processor and IBM's POWER8.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Learning Physically Interpretable Atmospheric Models From Data With WSINDy

The multiscale and turbulent nature of Earth's atmosphere has historically rendered accurate weather modeling a hard problem. Recently, there has been an explosion of interest surrounding data-driven approaches to weather modeling, which in many cases show improved forecasting accuracy and computational efficiency when compared to traditional methods. However, many of the current data-driven approaches employ highly parameterized neural networks, often resulting in uninterpretable models and limited gains in scientific understanding. In this work, we address the interpretability problem by explicitly discovering partial differential equations governing atmospheric phenomena, identifying symbolic mathematical models with direct physical interpretations. The purpose of this paper is to demonstrate that, in particular, the weak-form sparse identification of nonlinear dynamics (WSINDy) algorithm can learn effective atmospheric models from both simulated and assimilated data. Our approach adapts the standard WSINDy algorithm to work with high-dimensional fluid data of arbitrary spatial dimension.

58 GEOSCIENCES↗
DOE OSTI2022

Stellar kinematics of dwarf galaxies from multi-epoch spectroscopy: application to Triangulum II

ABSTRACT We present new MMT/Hectochelle spectroscopic measurements for 257 stars observed along the line of sight to the ultrafaint dwarf galaxy Triangulum II (Tri II). Combining results from previous Keck/DEIMOS spectroscopy, we obtain a sample that includes 16 likely members of Tri II, with up to 10 independent redshift measurements per star. To this multi-epoch kinematic data set, we apply methodology that we develop in order to infer binary orbital parameters from sparsely sampled radial velocity curves with as few as two epochs. For a previously identified (spatially unresolved) binary system in Tri II, we infer an orbital solution with period $296.0_{-3.3}^{+3.8} \rm ~ d$, semimajor axis $1.12^{+0.41}_{-0.24}\rm ~au$, and systemic velocity $-380.0 \pm 1.7 \rm ~km ~s^{-1}$ that we then use in the analysis of Tri II’s internal kinematics. Despite this improvement in the modelling of binary star systems, the current data remain insufficient to resolve the velocity dispersion of Tri II. We instead find a 95 per cent confidence upper limit of $\sigma _{v} \lesssim 3.4 \rm ~km~s^{-1}$.

Buttry, Rachel (ORCID:0000000155017609)↗
DOE OSTI2023

Wildfire smoke exposure and early childhood respiratory health: a study of prescription claims data

Abstract Wildfire smoke is associated with short-term respiratory outcomes including asthma exacerbation in children. As investigations into developmental wildfire smoke exposure on children’s longer-term respiratory health are sparse, we investigated associations between developmental wildfire smoke exposure and first use of respiratory medications. Prescription claims from IBM MarketScan Commercial Claims and Encounters database were linked with wildfire smoke plume data from NASA satellites based on Metropolitan Statistical Area (MSA). A retrospective cohort of live infants (2010–2016) born into MSAs in six western states (U.S.A.), having prescription insurance, and whose birthdate was estimable from claims data was constructed ( N = 184,703); of these, gestational age was estimated for 113,154 infants. The residential MSA, gestational age, and birthdate were used to estimate average weekly smoke exposure days ( smoke-day ) for each developmental period: three trimesters, and two sequential 12-week periods post-birth. Medications treating respiratory tract inflammation were classified using active ingredient and mode of administration into three categories:: 'upper respiratory', 'lower respiratory', 'systemic anti-inflammatory'. To evaluate associations between wildfire smoke exposure and medication usage, Cox models associating smoke-days with first observed prescription of each medication category were adjusted for infant sex, birth-season, and birthyear with a random intercept for MSA. Smoke exposure during postnatal periods was associated with earlier first use of upper respiratory medications (1–12 weeks: hazard ratio (HR) = 1.094 per 1-day increase in average weekly smoke-day, 95%CI: (1.005,1.191); 13–24 weeks: HR = 1.108, 95%CI: (1.016,1.209)). Protective associations were observed during gestational windows for both lower respiratory and systemic anti-inflammatory medications; it is possible that these associations may be a consequence of live-birth bias. These findings suggest wildfire smoke exposure during early postnatal developmental periods impact subsequent early life respiratory health.

Dhingra, Radhika↗
DOE OSTI2015

A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO 2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO 2 (ffCO 2 ) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO 2 emissions and synthetic observations of ffCO 2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations. Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach has potential implications in many fields in which the governing equations are well understood but the model uncertainty comes from unresolved physical processes. - Highlights: • Proposed a physics–informed framework to quantify uncertainty in RANS simulations. • Framework incorporates physical prior knowledge and observation data. • Based on a rigorous Bayesian framework yet fully utilizes physical model. • Applicable for many complex physical systems beyond turbulent flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Derivative-based SINDy (DSINDy): Addressing the challenge of discovering governing equations from noisy data

Recent advances in the field of data-driven dynamics allow for the discovery of ODE systems using state measurements. One approach, known as Sparse Identification of Nonlinear Dynamics (SINDy), assumes the dynamics are sparse within a predetermined basis in the states and finds the expansion coefficients through linear regression with sparsity constraints. This approach requires an accurate estimation of the state time derivatives, which is not necessarily possible in the high-noise regime without additional constraints. We present an approach called Derivative-based SINDy (DSINDy) that combines two novel methods to improve ODE recovery at high-noise levels. First, we denoise the state variables by applying a projection operator that leverages the assumed basis for the system dynamics. Second, we use a second order cone program (SOCP) to find the derivative and governing equations simultaneously. We derive theoretical results for the projection-based denoising step, which allow us to estimate the values of hyperparameters used in the SOCP formulation. This underlying theory helps limit the number of required user-specified parameters. Finally, we present results demonstrating that our approach leads to improved system recovery for the Van der Pol oscillator, the Duffing oscillator, the Rössler attractor, and the Lorenz 96 model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Tensor Analysis Methods for Activity Characterization in Spatiotemporal Data

Tensor (multiway array) factorization and decomposition offers unique advantages for activity characterization in spatio-temporal datasets because these methods are compatible with sparse matrices and maintain multiway structure that is otherwise lost in collapsing for regular matrix factorization. This report describes our research as part of the PANTHER LDRD Grand Challenge to develop a foundational basis of mathematical techniques and visualizations that enable unsophisticated users (e.g. users who are not steeped in the mathematical details of matrix algebra and mulitway computations) to discover hidden patterns in large spatiotemporal data sets.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2024

Beyond PCA: Additional Dimension Reduction Techniques to Consider in the Development of Climate Fingerprints

Abstract Dimension reduction techniques are an essential part of the climate analyst’s toolkit. Due to the enormous scale of climate data, dimension reduction methods are used to identify major patterns of variability within climate dynamics, to create compelling and informative visualizations, and to quantify major named modes such as El Niño–Southern Oscillation. Principal components analysis (PCA), also known as the method of empirical orthogonal functions (EOFs), is the most commonly used form of dimension reduction, characterized by a remarkable confluence of attractive mathematical, statistical, and computational properties. Despite its ubiquity, PCA suffers from several difficulties relevant to climate science: high computational burden with large datasets, decreased statistical accuracy in high dimensions, and difficulties comparing across multiple datasets. In this paper, we introduce several variants of PCA that are likely to be of use in climate sciences and address these problems. Specifically, we introduce non-negative , sparse , and tensor PCA and demonstrate how each approach provides superior pattern recognition in climate data. We also discuss approaches to comparing PCA-family results within and across datasets in a domain-relevant manner. We demonstrate these approaches through an analysis of several runs of the E3SM climate model from 1991 to 1995, focusing on the simulated response to the Mt. Pinatubo eruption; our findings are consistent with a recently identified stratospheric warming fingerprint associated with this type of stratospheric aerosol injection.

Weylandt, Michael↗
DOE OSTI2018

Partitioning and Communication Strategies for Sparse Non-negative Matrix Factorization

Non-negative matrix factorization (NMF), the problem of finding two non-negative low-rank factors whose product approximates an input matrix, is a useful tool for many data mining and scientific applications such as topic modeling in text mining and unmixing in microscopy. In this paper, we focus on scaling algorithms for NMF to very large sparse datasets and massively parallel machines by employing effective algorithms, communication patterns, and partitioning schemes that leverage the sparsity of the input matrix. We consider two previous works developed for related problems, one that uses a fine-grained partitioning strategy using a point-to-point communication pattern and one that uses a Cartesian, or checkerboard, partitioning strategy using a collective-based communication pattern. We show that a combination of the previous approaches balances the demands of the various computations within NMF algorithms and achieves high efficiency and scalability. From the experiments, we see that our proposed strategy runs up to 10x faster than the state of the art on real-world datasets.

Kaya, Oguz↗
DOE OSTI2016

ACHIEVING CONSISTENT DOPPLER MEASUREMENTS FROM SDO /HMI VECTOR FIELD INVERSIONS

NASA’s Solar Dynamics Observatory is delivering vector magnetic field observations of the full solar disk with unprecedented temporal and spatial resolution; however, the satellite is in a highly inclined geosynchronous orbit. The relative spacecraft–Sun velocity varies by ±3 km s{sup −1} over a day, which introduces major orbital artifacts in the Helioseismic Magnetic Imager (HMI) data. We demonstrate that the orbital artifacts contaminate all spatial and temporal scales in the data. We describe a newly developed three-stage procedure for mitigating these artifacts in the Doppler data obtained from the Milne–Eddington inversions in the HMI pipeline. The procedure ultimately uses 32 velocity-dependent coefficients to adjust 10 million pixels—a remarkably sparse correction model given the complexity of the orbital artifacts. This procedure was applied to full-disk images of AR 11084 to produce consistent Dopplergrams. The data adjustments reduce the power in the orbital artifacts by 31 dB. Furthermore, we analyze in detail the corrected images and show that our procedure greatly improves the temporal and spectral properties of the data without adding any new artifacts. We conclude that this new procedure makes a dramatic improvement in the consistency of the HMI data and in its usefulness for precision scientific studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

TH-A-18C-04: Ultrafast Cone-Beam CT Scatter Correction with GPU-Based Monte Carlo Simulation

Purpose: Scatter artifacts severely degrade image quality of cone-beam CT (CBCT). We present an ultrafast scatter correction framework by using GPU-based Monte Carlo (MC) simulation and prior patient CT image, aiming at automatically finish the whole process including both scatter correction and reconstructions within 30 seconds. Methods: The method consists of six steps: 1) FDK reconstruction using raw projection data; 2) Rigid Registration of planning CT to the FDK results; 3) MC scatter calculation at sparse view angles using the planning CT; 4) Interpolation of the calculated scatter signals to other angles; 5) Removal of scatter from the raw projections; 6) FDK reconstruction using the scatter-corrected projections. In addition to using GPU to accelerate MC photon simulations, we also use a small number of photons and a down-sampled CT image in simulation to further reduce computation time. A novel denoising algorithm is used to eliminate MC scatter noise caused by low photon numbers. The method is validated on head-and-neck cases with simulated and clinical data. Results: We have studied impacts of photo histories, volume down sampling factors on the accuracy of scatter estimation. The Fourier analysis was conducted to show that scatter images calculated at 31 angles are sufficient to restore those at all angles with <0.1% error. For the simulated case with a resolution of 512×512×100, we simulated 10M photons per angle. The total computation time is 23.77 seconds on a Nvidia GTX Titan GPU. The scatter-induced shading/cupping artifacts are substantially reduced, and the average HU error of a region-of-interest is reduced from 75.9 to 19.0 HU. Similar results were found for a real patient case. Conclusion: A practical ultrafast MC-based CBCT scatter correction scheme is developed. The whole process of scatter correction and reconstruction is accomplished within 30 seconds. This study is supported in part by NIH (1R01CA154747-01), The Core Technology Research in Strategic Emerging Industry, Guangdong, China (2011A081402003)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Integrating geophysical monitoring data into multiphase fluid flow reservoir simulation

Simulation of multiphase flow systems are of critical importance in managing hydrological systems. Flow simulations are affected by a number of factors including structure and flow properties including porosity and permeability as well as the anisotropy and heterogeneity of these properties. In many cases traditional hydrological and reservoir data are highly affected by these parameters, but are not directly sensitive to them. As such modellers often adjust these parameters in an ad hoc manner until solutions numerically converge. Simulation models are generally based on structural data from reflection seismics whose physical flow properties are then populated using geostatistical extrapolation techniques utilizing a sparse number of borehole logs and core analysis. In multiphase systems including enhanced oil recovery and carbon capture and sequestration uncertainties regarding phase-dependent physical properties confounds this challenge further. Geophysical methods provide a means by which to gain an improved understanding of phase distributions in the subsurface. In this paper we will look at applications from active carbon capture and sequestration and enhanced oil recovery applications, as well as synthetic examples. Geophysical data including electromagnetic and gravity are inverted using structural constraints from the reservoir model. Inversions are then mapped into flow properties using calibrated relations such as Archie’s Equation. The coupled models can then be used to both verify and improve on the reservoir flow model which improves it’s predictive power and utility as a management tool.

02 PETROLEUM↗
DOE OSTI2016

Energy Dependence of Fission Product Yields from {sup 235}U, {sup 238}U and {sup 239}Pu for Incident Neutron Energies Between 0.5 and 14.8 MeV

Fission Product Yields (FPY) have historically been one of the most observable features of the fission process. They are known to have strong variations that are dependent on the fissioning species, the excitation energy, and the angular momentum of the compound system. However, consistent and systematic studies of the variation of these FPY with energy have proved challenging. This is caused primarily by the nature of the experiments that have traditionally relied on radiochemical procedures to isolate specific fission products. Although radiochemical procedures exist that can isolate all products, each element presents specific challenges and introduces varying degrees of systematic errors that can make inter-comparison of FPY uncertain. Although of high importance in fields such as nuclear forensics and Stockpile Stewardship, accurate information about the energy dependence of neutron induced FPY are sparse, due primarily to the lack of suitable monoenergetic neutron sources. There is a clear need for improved data, and to address this issue, a collaboration was formed between Los Alamos National Laboratory (LANL), Lawrence Livermore National Laboratory (LLNL) and the Triangle Universities Nuclear Laboratory (TUNL) to measure the energy dependence of FPY for {sup 235}U, {sup 238}U and {sup 239}Pu. The measurements have been performed at TUNL, using a 10 MV Tandem Van de Graaff accelerator to produce monoenergetic neutrons at energies between 0.6 MeV to 14.8 MeV through a variety of reactions. The measurements have utilized a dual-fission chamber, with thin (10-100 μg/cm2) reference foils of similar material to a thick (100-400 mg) activation target held in the center between the chambers. This method allows for the accurate determination of the number of fissions that occurred in the thick target without requiring knowledge of the fission cross section or neutron fluence on target. Following activation, the thick target was removed from the dual-fission chamber and gamma-ray counted using shielded HPGe detectors for a period of 1-2 months to determine the yield of various fission products. To the extent possible all irradiation and counting procedures were kept the same to minimize sources of systematic errors. FPY have been determined at incident neutron energies of 0.6, 1.4, 2.4, 3.5, 4.6, 5.5, 8.9 and 14.8 MeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Sparsity and Nullity: Paradigms for Analysis Dictionary Learning

Sparse models in dictionary learning have been successfully applied in a wide variety of machine learning and computer vision problems, and as a result have recently attracted increased research interest. Another interesting related problem based on linear equality constraints, namely the sparse null space (SNS) problem, first appeared in 1986 and has since inspired results on sparse basis pursuit. In this work, we investigate the relation between the SNS problem and the analysis dictionary learning (ADL) problem, and show that the SNS problem plays a central role, and may be utilized to solve dictionary learning problems. Moreover, we propose an efficient algorithm of sparse null space basis pursuit (SNS-BP) and extend it to a solution of ADL. Lastly, experimental results on numerical synthetic data and real-world data are further presented to validate the performance of our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means

This work addresses segmentation of volumetric images of woven carbon fiber textiles from micro-tomography data. We propose a semi-supervised algorithm to classify carbon fibers that requires sparse input as opposed to completely labeled images. The main contributions are: (a) design of effective discriminative classifiers, for three-dimensional textile samples, trained on wavelet features for segmentation; (b) coupling of previous step with nonlocal means as simple, efficient alternative to the Potts model; and (c) demonstration of reuse of classifier to diverse samples containing similar content. We evaluate our work by curating test sets of voxels in the absence of a complete ground truth mask. The algorithm obtains an average 0.95 F1 score on test sets and average F1 score of 0.93 on new samples. Finally, we conclude with discussion of failure cases and propose future directions toward analysis of spatiotemporal high-resolution micro-tomography images.

3D image processing↗