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At least 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantum optimal control of superconducting qubits based on machine-learning characterization

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations in detailed numerical simulations of a superconducting transmon qubit. Furthermore, we demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of ∼99.99%.

Artificial neural networks↗
DOE OSTI2022

A Scalable Pipeline for Gigapixel Whole Slide Imaging Analysis on Leadership Class HPC Systems

Whole Slide Imaging (WSI) captures microscopic details of a patient's histopathological features at multiple res-olutions organized across different levels. Images produced by WSI are gigapixel-sized, and saving a single image in memory requires a few gigabytes which is scarce since a complicated model occupies tens of gigabytes. Performing a simple met-ric operation on these large images is also expensive. High-performance computing (HPC) can help us quickly analyze such large images using distributed training of complex deep learning models. One popular approach in analyzing these images is to divide a WSI image into smaller tiles (patches) and then train a simpler model with these reduced-sized but large numbers of patches. However, we need to solve three pre-processing challenges efficiently for pursuing this patch-based approach. 1) Creating small patches from a high-resolution image can result in a high number (hundreds of thousands per image) of patches. Storing and processing these images can be challenging due to a large number of I/O and arithmetic operations. To reduce I/Oand memory accesses, an optimal balance between the size and number of patches must exist to reduce I/O and memory accesses. 2) WSI images may have tiny annotated regions for cancer tissue and a significant portion with normal and fatty tissues; correct patch sampling should avoid dataset imbalance. 3) storing and retrieving many patches to and from disk storage might incur I/O latency while training a deep learning model. An efficient distributed data loader should reduce I/O latency during the training and inference steps. This paper explores these three challenges and provides empirical and algorithmic solutions deployed on the Summit supercomputer hosted at the Oak Ridge Leadership Computing Facility.

Dash, Sajal↗
DOE OSTI2021

epi_inference v. 1.0

SAND2021-0503 O The epi_inference software supports the prediction of disease transmission parameters from data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hart, William↗
DOE OSTI2023

baseflow: a MATLAB and GNU Octave package for baseflow recession analysis

baseflow is a MATLAB® toolbox designed for baseflow recession analysis, a technique used in hydrologic science to infer aquifer properties from streamflow. By leveraging widely available streamflow data, baseflow can be used to estimate aquifer properties such as hydraulic conductivity and drainable porosity over the modern instrumental stream gage record. The toolbox is intended for analysis of measured streamflow values recorded on a daily timestep, and is tailored for shallow, unconfined riparian aquifers that discharge groundwater laterally into adjacent streams. Additionally, baseflow can analyze the collective behavior of individual hillslope aquifers constituting hydrologic catchments, known as “watersheds”, from a nonlinear dynamical systems perspective. The toolbox incorporates recent advances in baseflow recession analysis to enable objective estimations of aquifer properties, and their sensitivity to methodological decisions, at both hillslope and catchment scales.

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 OSTI2025

Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs

I show how to compute the nonlinear power spectrum across the entire $w(z)$ dynamical dark energy model space. Using synthetic ΛCDM data, I train a neural ordinary differential equation (ODE) to infer the evolution of the nonlinear matter power spectrum as a function of the background expansion and mean matter density across ∼9 Gyr of cosmic evolution. After training, the model generalises to any dynamical dark energy model parameterised by $w(z)$. With little optimisation, the neural ODE is accurate to within 4% up to $k = 5\, h\, {\mathrm Mpc}^{−1}$. Unlike simulation rescaling methods, neural ODEs naturally extend to summary statistics beyond the power spectrum that are sensitive to the growth history.

cosmology↗
DOE OSTI2022

Generation of Data-Driven Expected Energy Models for Photovoltaic Systems

Although unique expected energy models can be generated for a given photovoltaic (PV) site, a standardized model is also needed to facilitate performance comparisons across fleets. Current standardized expected energy models for PV work well with sparse data, but they have demonstrated significant over-estimations, which impacts accurate diagnoses of field operations and maintenance issues. This research addresses this issue by using machine learning to develop a data-driven expected energy model that can more accurately generate inferences for energy production of PV systems. Irradiance and system capacity information was used from 172 sites across the United States to train a series of models using Lasso linear regression. The trained models generally perform better than the commonly used expected energy model from international standard (IEC 61724-1), with the two highest performing models ranging in model complexity from a third-order polynomial with 10 parameters (Radj2 = 0.994) to a simpler, second-order polynomial with 4 parameters (Radj2=0.993), the latter of which is subject to further evaluation. Subsequently, the trained models provide a more robust basis for identifying potential energy anomalies for operations and maintenance activities as well as informing planning-related financial assessments. We conclude with directions for future research, such as using splines to improve model continuity and better capture systems with low (≤1000 kW DC) capacity.

14 SOLAR ENERGY↗
DOE OSTI2022

A Reanalysis of Public Galactic Bulge Gravitational Microlensing Events from OGLE-III and -IV

Abstract Modern surveys of gravitational microlensing events have progressed to detecting thousands per year, and surveys are capable of probing Galactic structure, stellar evolution, lens populations, black hole physics, and the nature of dark matter. One of the key avenues for doing this is the microlensing Einstein radius crossing time (t E) distribution. However, systematics in individual light curves as well as oversimplistic modeling can lead to biased results. To address this, we developed a model to simultaneously handle the microlensing parallax due to Earth's motion, systematic instrumental effects, and unlensed stellar variability with a Gaussian process model. We used light curves for nearly 10,000 OGLE-III and -IV Milky Way bulge microlensing events and fit each with our model. We also developed a forward model approach to infer the t E distribution by forward modeling from the data rather than using point estimates from individual events. We find that modeling the variability in the baseline removes a source of significant bias in individual events, and the previous analyses overestimated the number of t E > 100 day events due to their oversimplistic model ignoring parallax effects. We use our fits to identify the hundreds filling a regime in the microlensing parameter space that are 50% pure of black holes. Finally, we have released the largest-ever catalog of Markov Chain Monte Carlo parameter estimates for microlensing events.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1975

Martian atmospheric lee waves

Mariner 9 television pictures of Mars extensive mountain lee wave phenomenon in the northern mid-latitudes during winter were evaluated. The characteristic wave length of the lee waves is readily observable, and in a few cases the boundaries of the wave patterns, as well as the wave length, are observed. The cloud patterns resulting from the waves generated by the flow across a mountain or crater are shown to be dependent upon the velocity profile of the air stream and the vertical stability of the atmosphere. Using the stability as inferred by the temperature structure obtained from the infrared spectrometer data, a two layer velocity model of the air stream is used in calculations based on the theory of mountain lee waves. Results yield magnitudes generally in agreement with various other circulation models.

Pirraglia, J. A.↗
NASA NTRS1977

Measurements of the O+ plus N2 and O+ plus O2 reaction rates from 300 to 900 K

Rate coefficients for the O(+) + N2 atom transfer and O(+) + O2 charge transfer reactions are determined at thermal energies between 300 K and 900 K difference in a heated drift tube mass spectrometer apparatus. At 300 K the values K(O(+) + N2) = (1.2 plus or minus 0.1) x 10 to the negative 12 power cubic cm/sec and k(O(+) + O2) = (2.1 plus or minus 0.2) x 10 to the negative 11 power cubic cm/sec were obtained, with a 50% difference decrease in the reaction rates upon heating to 700 K. These results are in good agreement with heated flowing afterglow results, but the O(+) + O2 thermal rate coefficients are systematically lower than equivalent Maxwellian rates inferred by conversion of nonthermal drift tube and flow drift data.

Chen, A.↗
NASA NTRS1978

Isotopic anomalies from neutron reactions during explosive carbon burning

The possibility that the newly discovered correlated isotopic anomalies for heavy elements in the Allende meteorite were synthesized in the secondary neutron capture episode during the explosive carbon burning, the possible source of the O-16 and Al-26 anomalies, is examined. Explosive carbon burning calculations under typical conditions were first performed to generate time profiles of temperature, density, and free particle concentrations. These quantities were inputted into a general neutron capture code which calculates the resulting isotopic pattern from exposing the preexisting heavy seed nuclei to these free particles during the explosive carbon burning conditions. The interpretation avoids the problem of the Sr isotopic data and may resolve the conflict between the time scales inferred from 1-129, Pu-244, and Al-26.

Lee, T.↗
NASA NTRS1979

Application of the trap-plus-precipitation hard X-ray burst model to the flare of August 4, 1972

The theory of Melrose and Brown (1976) concerning the X-ray spectrum resulting from combined precipitation and in-trap acceleration of nonthermal electrons is applied to a large solar flare on the basis of high-time-resolution hard X-ray data. The time behavior of the electron source function is inferred as a function of energy and mean ambient trap density. The results are interpreted in the context of a purely thick-target model, a static trap with continuous injection of fresh electrons, and a dynamic trap with continuous redistribution of electron energies. It is concluded that: (1) a minimum trap density exists below which the static-trap description cannot be valid; (2) a mean ambient trap density less than the cited minimum is possible if a dynamic trap is invoked; and (3) the electron source function at a given instant in such low-density dynamic traps may bear little or no resemblance to a power-law spectrum despite its production, via nonthermal bremsstrahlung, of a power-law X-ray spectrum.

Emslie, A. G.↗
NASA NTRS1981

Contact binary stars

Densities, corrected primary colors, minimum periods, inferred masses, luminosities, and specific angular momenta are computed from data on 37 W Ursae Majoris systems. A-type systems, having lower densities and angular momenta than the W-type systems, are shown to be evolved, and a new class of contact binary is identified, the OO Aquilae systems, whose members have evolved into contact. Evolutionary grids based on the contact condition agree with observation, except in that the evolved A-type systems have lost more angular momentum than predicted by gravitational radiation alone. This is accounted for by stellar wind magnetic braking, which is shown to be effective on a shorter time scale and to be important in other kinds of binaries containing a cool, tidally coupled component.

Mochnacki, S. W.↗
NASA NTRS1982

Design and calibration of a coherent lidar for measurement of atmospheric backscatter

This paper discusses the background requirements for, and the design and calibration of a coherent Doppler lidar which is oriented specifically toward the measurement of low values of atmospheric backscatter at a wavelength of 10.6 microns. The lidar is a compact, continuous wave system which can operate in two modes, multiple particle scattering and single particle scattering. In the multiple scattering mode, the lidar employs an extended focal volume and utilizes a technique similar to Dicke switching to achieve measurement of the volume backscatter. In the single scattering mode, the focal volume is reduced to ensure a high probability of single particle scattering. Measurement of the single particle backscatter as a function of time leads to the formation of signal histogram from which the volume backscatter can be inferred. In addition to providing the atmospheric backscatter value, appropriate data processing algorithms and focal volume calibration allow the single particle mode to yield information on the atmospheric aerosol scattering cross-section distribution.

Jones, W. D.↗
NASA NTRS1983

Peculiar ultraviolet interstellar extinction

In connection with investigations of the physical characteristics of interstellar dust, a study of objects with peculiar extinction might provide new insights about the composition, size distribution, formation, and destruction of the particulate matter. Meyer and Savage (1981) have found that the dust toward many stars exhibits peculiar UV extinction. The present investigation is concerned with International Ultraviolet Explorer (IUE) spectrophotometric measurements for a sample of stars judged by Meyer and Savage to have highly anomalous UV extinction as inferred from the broad-band Astronomical Netherlands Satellite (ANS) data. The reported IUE measurements provide the opportunity to determine if the ANS photometry measurements of Meyer and Savage are anomalous because of peculiar extinction.

Massa, D.↗
NASA NTRS1984

The asteroidal source region of ordinary chondrites

The final, Earth-impacting orbits of ordinary chondritic meteorites have a very special distribution. By use of visual radiant and time of fall data, as well as photographic fireball orbits (1) it is inferred that chondrite perihelia are concentrated near 1 A.U., eccentricities are usually rather high (approximately 0.5), and inclinations are low (approximately 10 deg). Velocity selection resulting from atmospheric ablation plays a significant role in determining this orbital distribution, but by no means suffices to explain it. The observed distribution is a fragile one, and can easily be destroyed by Earth and Venus perturbations. This places severe constraints on the location of the original source bodies, of which these meteorites are fragments. New calculations were made of the expected distribution of final orbits from a range of initial sources, taking into consideration close encounter planetary perturbations, secular resonance, destruction by collision in space, and atmospheric ablation.

Wetherill, G. W.↗
NASA NTRS1985

Population mechanisms for the He(+) n = 3 levels determined from measurements of the solar 1640 A emission

The 1640 A line profile is analyzed using solar data obtained by the NRL slit spectrograph on Skylab. Data from coronal hole regions, quiet sun regions, active regions, prominence regions, and flare regions are presented. The relative densities of 3s, 3p, and 3d levels are determined from the data, and the dominant population mechanisms for these levels are inferred using a rate equation model for the He(+) ion. The relative importance of collisional excitation, radiative recombination, and photoexcitation is determined for each of the solar regions.

Seely, J. F.↗
NASA NTRS1989

Global, seasonal cloud variations from satellite radiance measurements. I - Sensitivity of analysis

Global, daily, visible and IR radiance measurements from the NOAA-5 Scanning Radiometer (SR) are analyzed for the months of January, April, July and October, 1977 to infer cloud and surface radiative properties. In this paper, the data and analysis method are described. A unique feature of the method is that it utilizes radiative transfer models that simulate the SR measurements using explicit parameters representing the properties of the surface, atmosphere, and clouds. The accuracy of all the results depends primarily on the proper separation of the total radiance distribution into those parts representing clear and cloudy scenes.

Rossow, William B.↗