Search NASA⌕ Search

SEARCH · Search NASA

Results for “Mathematics and Computing, Energy, Computer science, Energy - Solar”

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

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

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

House Advantage or House of Cards? Stacking the Deck for Data Videos Leads to Null Results: Preprint

Videos are becoming a ubiquitous means of sharing information on social media platforms. In response, data videos - short clips combining visualization with dynamic storytelling, audio descriptions, and spatial referencing - have gained popularity for communicating data. These affordances suggest that data videos might communicate data patterns, trends, and concepts more effectively than static visualizations, enhancing comprehension. However, existing research has not systematically tested this claim. To address this gap, we conducted three controlled studies to measure comprehension differences between data videos and static visualizations. Despite leveraging visual cues and audio explanations, no data video led to significantly better comprehension than an analogous static visualization. Our results suggest data videos are not categorically better and that future research should examine the tradeoffs between their engagement benefits and costs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

It is not σ 8 : constraining the non-linear matter power spectrum with the Dark Energy Survey Year-5 supernova sample

The weak gravitational lensing magnification of Type Ia supernovae (SNe Ia) is sensitive to the matter power spectrum on scales $k\gt 1 h$ Mpc$^{-1}$, making it unwise to interpret SNe Ia lensing in terms of power on linear scales. We compute the probability density function of SNe Ia magnification as a function of standard cosmological parameters, plus an empirical parameter $A_{\rm mod}$ which describes the suppression or enhancement of matter power on non-linear scales compared to a cold dark matter only model. While baryons are expected to enhance power on the scales relevant to SN Ia lensing, other physics such as neutrino masses or non-standard dark matter may suppress power. Using the Dark Energy Survey Year-5 sample, we find $A_{\rm mod} = 0.77^{+0.69}_{-0.40}$ (68 per cent credible interval around the median). Although the median is consistent with unity there are hints of power suppression, with $A_{\rm mod} \lt 1.09$ at 68 per cent credibility.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

2023 Operational Assessment Report: Argonne Leadership Computing Facility

In 2004, the U.S. Department of Energy’s (DOE’s) Advanced Scientific Computing Research (ASCR) program established the Leadership Computing Facility (LCF) with a mission to provide the world’s most advanced computational resources to the open science community. The LCF is a huge investment in the nation’s scientific and technological future, inspired by a growing demand for large-scale computing and its impact on science and engineering. The LCF operates two world-class centers in support of open science at Argonne National Laboratory (Argonne) and at Oak Ridge National Laboratory (Oak Ridge) and deploys diverse machines that are among the most powerful systems in the world today. The LCF ranks among the top U.S. scientific facilities delivering impactful science. The work performed at these centers informs policy decisions and advances innovations in far-reaching areas such as energy assurance, ecological sustainability, and global security. The leadership-class systems at Argonne and Oak Ridge operate around the clock every day of the year. The high level of services these centers provide and the exceptional science they produce justify their existence to the DOE Office of Science and the U.S. Congress

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Energy and physical resource impacts of quantum computing merit greater attention

Quantum computing research and development is growing worldwide; yet the energy and physical resource demands of future quantum-accelerated data centres are unknown. Planning for quantum computing requires strong collaboration between research communities across engineering, physics, environmental sciences, economics, policy, and energy systems and scenario modelling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Bridging the time scale in exascale computing of chemical systems (Final Technical Report)

This report summarizes the work carried out with support of the United States Department of Energy under Award DE-SC0019441. The theme of this project was to develop and apply methods that allowed for the acceleration of atomistic calculations, particularly in challenging areas such as multiphase systems, electrified interfaces, uncertainty estimation, and applications requiring chemical accuracy, which tend to be applications where simulation time is severely bottlenecked by the computational time requirements. Much of the focus was on the application of emerging machine-learning methodologies, although a wide range of methodologies were employed. This report has two major sections. The first focuses on the methodological advances themselves. Within this part, we report a number of major advances, a few examples of which are described here. We report the first machine-learning scheme for the acceleration of electronically grand-canonical calculations (that is, those applicable to electrochemistry). We report new methods of performing transfer learning, in which physics-based priors can be used to provide predictions, often with uncertainty estimates, of images well outside of training sets; we also offer ways to fine-tune these transfer-learning models. We provide a new systematic means to generate and apply minimal training data sets to very large (10,000’s of atoms) systems, with only small training sets appropriate for electronic structure. We developed new methodologies to integrate surface vibrations into surface adsorption calculations. We made advances to the applicability of diffusion Monte Carlo methods to allow (learned) force prediction, finite-size error correction, and force-free means of searching for transition states. We integrated machine-learned atomistic predictions into mechanism generation codes. Additionally, we released new software including AmpTorch, a modernized version of our original atomistic machine-learning code Amp. The second part of this report focuses on the scientific applications that accompanied, and were often enabled by, the methodological advances described earlier. A few examples follow, but full details are in the individual chapters of the report. For example, we developed a general theory of phonon-induced friction on molecular adsorbates. We showed fundamentally how solvent influences the adsorption and desorption process and how it differs from the processes typically involved at the solid–gas interface, making aqueous-phase and electrocatalysis different from traditional thermocatalysis. We examined how metal–insulator and magnetic transitions can be probed, and accelerated exciton dynamics via Frenkel Hamiltonian parameters. We showed that the nearsighted force-training approach, developed within this project, can predict both the stability and reactivity of large nanoparticles, and can also lead to insights on catalyst coverage on binding energies and entropies. These applied studies, which generally integrated with our method development, allowed us to push forward the theoretical understanding of several reaction classes.

08 HYDROGEN↗
DOE OSTI2024

Impact of Sr-Containing Secondary Phases on Oxide Conductivity in Solid-Oxide Electrolyzer Cells

Solid-oxide electrolyzer cells (SOECs) based on a yttria-stabilized zirconia (YSZ) oxide electrolyte produce hydrogen from water with the assistance of excess thermal energy; however, Sr diffusion within the Gd-doped CeO 2 (GDC) barrier layer during processing or operation can lead to the formation of unwanted secondary phases such as SrO and SrZrO 3 . Here, to establish and compare the degree of impact of these phases on SOEC performance, we conduct first-principles calculations to study their bulk oxide conductivities and compare them to that of the YSZ electrolyte. We find that SrO has a low conductivity arising from the poor mobility and low concentration of mobile oxygen vacancies, and its presence in SOECs should therefore be avoided. SrZrO 3 also has a lower oxide conductivity than YSZ; however, this discrepancy is primarily due to lower vacancy concentrations rather than low mobility. We find that sufficient levels of Y-doping on the Zr site can increase oxygen vacancy concentrations in SrZrO 3 to achieve an oxide ionic conductivity on par with that of YSZ, thereby mitigating any potential deleterious effect on transport performance. Energy-dispersive X-ray spectroscopy confirms that Y is the most common minority element present in SrZrO 3 forming near the GDC–YSZ interface, alleviating concerns regarding the impact of SrZrO 3 on device performance. These results from our combined computational–experimental analysis can inform future engineering strategies designed to limit the detrimental effects of Sr-induced secondary phase formation on SOEC performance.

08 HYDROGEN↗
DOE OSTI2025

Fusion and Fission Energy and Science Directorate and Information Technology Services Directorate HPC Cluster Reduction, Consolidation, and Savings in Data Center Space, Power, and Cooling

This report evaluates the benefits of decommissioning six legacy FFESD purchased HPC clusters and consolidating services and workloads into a new HPC cluster named HELIOS. The findings demonstrate significant reductions in the data center power and cooling requirements, data center footprint, and operational overhead, while simultaneously increasing computational capacity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Evaluating cosmological biases using photometric redshifts for Type Ia Supernova cosmology with the Dark Energy Survey Supernova Program

Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying the type of supernova and obtaining reliable redshifts to measure the distance–redshift relation. While obtaining a host-galaxy spectroscopic redshift for most SNe is feasible for small-area transient surveys, it will be too resource intensive for upcoming large-area surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time, which will observe on the order of millions of SNe. Here, we use data from the Dark Energy Survey (DES) to address this problem with photometric redshifts (photo-z) inferred directly from the SN light curve in combination with Gaussian and full p(z) priors from host-galaxy photo-z estimates. Using the DES 5-yr photometrically classified SN sample, we consider several photo-z algorithms as host-galaxy photo-z priors, including the Self-Organizing Map redshifts (SOMPZ), Bayesian Photometric Redshifts (BPZ), and Directional-Neighbourhood Fitting (DNF) redshift estimates employed in the DES 3 × 2 point analyses. With detailed catalogue-level simulations of the DES 5-yr sample, we find that the simulated w can be recovered within ±0.02 when using SN+SOMPZ or DNF prior photo-z, smaller than the average statistical uncertainty for these samples of 0.03. With data, we obtain biases in w consistent with simulations within ~1σ for three of the five photo-z variants. We further evaluate how photo-z systematics interplay with photometric classification and find classification introduces a subdominant systematic component. This work lays the foundation for next-generation fully photometric SNe Ia cosmological analyses.

(cosmology:) dark energy↗
DOE OSTI2025

Solving sparse finite element problems on neuromorphic hardware

The finite element method (FEM) is one of the most important and ubiquitous numerical methods for solving partial differential equations (PDEs) on computers for scientific and engineering discovery. Applying the FEM to larger and more detailed scientific models has driven advances in high-performance computing for decades. Here we demonstrate that scalable spiking neuromorphic hardware can directly implement the FEM by constructing a spiking neural network that solves the large, sparse, linear systems of equations at the core of the FEM. We show that for the Poisson equation, a fundamental PDE in science and engineering, our neural circuit achieves meaningful levels of numerical accuracy and close to ideal scaling on modern, inherently parallel and energy-efficient neuromorphic hardware, specifically Intel’s Loihi 2 neuromorphic platform. We illustrate extensions to irregular mesh geometries in both two and three dimensions as well as other PDEs such as linear elasticity. Our spiking neural network is constructed from a recurrent network model of the brain’s motor cortex and, in contrast to black-box deep artificial neural network-based methods for PDEs, directly translates the well-understood and trusted mathematics of the FEM to a natively spiking neuromorphic algorithm.

Applied mathematics↗
DOE OSTI2025

ComPort: Rigorous Testing Methods to Safeguard Software Porting (Final UW Report)

This report summarizes the University of Washington’s contributions to the ComPort project, Rigorous Testing Methods to Safeguard Software Porting, funded under the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR), award number DE-SC0022081. UW’s work addressed numerics-driven correctness challenges arising in heterogeneous and rapidly evolving computing environments. Our efforts focused on developing interactive analysis tools, correctness-preserving rewriting systems, semantic-aware code-transformation infrastructure, and multi-language support for reproducible numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models

Abstract Background Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). Here, we describe an FPGA hardware accelerator, called HAVAC, that targets a key bottleneck step (SSV) in the analysis pipeline of the popular pHMM alignment tool, HMMER. Results The HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a $$\sim$$ ∼ $3000 Xilinx Alveo U50 FPGA accelerator card, $$\sim$$ ∼ 227× faster than the optimized SSV implementation in nhmmer . Accounting for PCI-e data transfer data processing, HAVAC is 65× faster than nhmmer’s SSV with one thread and 35× faster than nhmmer with four threads, and uses $$\sim$$ ∼ 31% the energy of a traditional high end Intel CPU. Conclusions HAVAC demonstrates the potential offered by FPGA hardware accelerators to produce dramatic speed gains in sequence annotation and related bioinformatics applications. Because these computations are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects of the analysis pipeline.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

String Data 2023 (Conference)

The annual String Data conferences have become the flagship annual meeting for the subfield at the interface of formal high energy theory, pure mathematics, and machine learning. String Data 2023 featured invited plenary talks by leading researchers in addition to a parallel session. The funds helped mitigate conference planning and provided support to young researchers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1998

On the Tropical Rainfall Measuring Mission (TRMM): Bringing NASA's Earth System Science Program to the Classroom

The Tropical Rainfall Measuring Mission is the first mission dedicated to measuring tropical and subtropical rainfall using a variety of remote sensing instrumentation, including the first spaceborne rain-measuring radar. Since the energy released when tropical rainfall occurs is a primary "fuel" supply for the weather and climate "engine"; improvements in computer models which predict future weather and climate states may depend on better measurements of global tropical rainfall and its energy. In support of the STANYS conference theme of Education and Space, this presentation focuses on one aspect of NASA's Earth Systems Science Program. We seek to present an overview of the TRMM mission. This overview will discuss the scientific motivation for TRMM, the TRMM instrument package, and recent images from tropical rainfall systems and hurricanes. The presentation also targets educational components of the TRMM mission in the areas of weather, mathematics, technology, and geography that can be used by secondary school/high school educators in the classroom.

Shepherd, J. Marshall↗
DOE OSTI2025

Comparing the DES-SN5YR and Pantheon+ SN cosmology analyses: investigation based on ‘evolving dark energy or supernovae systematics’?

Recent cosmological analyses measuring distances of type Ia supernovae (SNe Ia) and baryon acoustic oscillations (BAO) have all given similar hints at time-evolving dark energy. To examine whether underestimated SN Ia systematics might be driving these results, Efstathiou (2025) compared overlapping SN events between Pantheon+ and DES-SN5YR (20 per cent SNe are in common), and reported evidence for an $\sim$0.04 mag offset between the low- and high-redshift distance measurements of this subsample of events. If this offset is arbitrarily subtracted from the entire DES-SN5YR sample, the preference for evolving dark energy is reduced. In this paper, we show that this offset is mostly due to different corrections for Malmquist bias between the two samples; therefore, an object-to-object comparison can be misleading. Malmquist bias corrections differ between the two analyses for several reasons. First, DES-SN5YR used an improved model of SN Ia luminosity scatter compared to Pantheon+ but the associated scatter-model uncertainties are included in the error budget. Secondly, improvements in host mass estimates in DES-SN5YR also affected SN standardized magnitudes and their bias corrections. Thirdly, and most importantly, the selection functions of the two compilations are significantly different, hence the inferred Malmquist bias corrections. Even if the original scatter model and host properties from Pantheon+ are used instead, the evidence for evolving dark energy from CMB, DESI BAO Year 1 and DES-SN5YR is only reduced from 3.9$\sigma$ to 3.3$\sigma$, consistent with the error budget. Finally, in this investigation, we identify an underestimated systematic uncertainty related to host galaxy property uncertainties, which could increase the final DES-SN5YR error budget by 3 per cent. In conclusion, we confirm the validity of the published DES-SN5YR results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Creation of a Weather Drivers Test Suite for Inclusion in ASHRAE Standard 140

Weather conditions are an important boundary condition for building performance simulation (BPS) calculations. For existing test cases in ASHRAE Standard 140 "Method of Test for Evaluating Building Performance Simulation Software" (ANSI/ASHRAE 2020), it was assumed that the software being tested could adequately read and interpret the weather data in the provided standard weather files. As differences between the programs have been reduced and as more programs have shifted to sub-hourly time steps this assumption has become more stretched. To address these concerns a new test suite testing a program's ability to read and interpret the data from a standard weather file was developed. The purpose of the test suite is to test the use of the typical data used from standard weather files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The hierarchical growth of bright central galaxies and intracluster light as traced by the magnitude gap

Using a sample of 2800 galaxy clusters identified in the Dark Energy Survey across the redshift range 0.20 < z < 0.60, we characterize the hierarchical assembly of bright central galaxies (BCGs) and the surrounding intracluster light (ICL). To quantify hierarchical formation we use the stellar mass–halo mass (SMHM) relation, comparing the halo mass, estimated via the mass–richness relation, to the stellar mass within the BCG + ICL system. Moreover, we incorporate the magnitude gap (M14), the difference in brightness between the BCG (measured within 30 kpc) and fourth brightest cluster member galaxy within 0.5 $R_{200,c}$, as a third parameter in this linear relation. The inclusion of M14, which traces BCG hierarchical growth, increases the slope and decreases the intrinsic scatter, highlighting that it is a latent variable within the BCG + ICL SMHM relation. Moreover, the correlation with M14 decreases at large radii. However, the stellar light within the BCG + ICL transition region (30 –80 kpc) most strongly correlates with halo mass and has a statistically significant correlation with M14. Since the transition region and M14 are independent measurements, the transition region may grow due to the BCG’s hierarchical formation. Additionally, as M14 and ICL result from hierarchical growth, we use a stacked sample and find that clusters with large M14 values are characterized by larger ICL and BCG + ICL fractions, which illustrates that the merger processes that build the BCG stellar mass also grow the ICL. Furthermore, this may suggest that M14 combined with the ICL fraction can identify dynamically relaxed clusters.

79 ASTRONOMY AND ASTROPHYSICS↗