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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Exploring Uncertainty in Moment Estimation for Small Earthquakes in Southern Nevada Using the Coda Envelope Method

Compiling source parameter estimates for small earthquakes is important both for our understanding of earthquake physics and for accurately assessing earthquake hazard. Reliable source parameter estimates are difficult to achieve for small earthquakes, in part due to our inability to accurately model the relevant physical processes at high frequencies. The coda envelope methodology developed by Mayeda and Walter (1996) and Mayeda et al. (2003) can mitigate this concern and estimate the moment of small earthquakes by determining the parameters that control the shape of the S-wave coda envelope while eliminating path effects by minimizing the scatter between seismic stations. Here, we use an open-source implementation of this technique called the Coda Calibration Tool (CCT; Barno, 2017) to calculate CCT-based moment magnitude estimates of small earthquakes (M L 0–3) in the Rock Valley, Nevada, region within the Nevada National Security Site. The Rock Valley data set is of particular interest because it allows us to explore the changes in uncertainties of the coda calibration method with earthquake size and depth. We found that a consistent linear relationship exists between the local magnitude M L and our coda-derived M w estimates for earthquakes as small as M L 0–3, but that current CCT workflows do not accurately characterize very shallow events. We also demonstrate that the epistemic uncertainty in the apparent stress value assumed by the CCT algorithm can influence magnitude estimates of small earthquakes. In conclusion, these results provide valuable insight into the seismicity of this region, and inform future analysis and modeling efforts for nuclear monitoring and seismic hazard.

58 GEOSCIENCES↗
DOE OSTI2021

Assessing biogeographic survey gaps in bacterial diversity knowledge: A global synthesis of freshwaters

Freshwaters account for 0.8% of Earth's surface area, yet support >10% of known plant and animal species making them disproportionately biodiverse. Modern molecular techniques have begun to reveal microbial diversity, but application of these approaches to address global microbial biogeography is relatively unknown in freshwaters. Our aim was to identify gaps in microbial data coverage along climatic and landscape disturbance gradients and among terrestrial biomes and hydrographic regions for all freshwater ecosystems and three freshwater habitat types: lakes and reservoirs (lentic); streams and rivers (lotic); and wetlands. We reviewed literature on microbial diversity in freshwaters surveyed using 16S ribosomal RNA sequencing which identify microbial taxa. We georeferenced survey locations and used a geographic information system to identify and map gaps in survey coverage using open-source data for climate, landscape disturbance, terrestrial biomes, and freshwater ecoregions. In our study, we compiled 3,425 georeferenced survey locations reported from 963 studies. Streams were surveyed most frequently (60.8% of survey locations), followed by lakes (33.5%) and wetlands (5.6%). Surveys were concentrated in North America, central and western Europe, and Southeast Asia; 35% of freshwater ecoregions were surveyed at least once across freshwater habitat types, whereas 23%, 23%, and 12% were surveyed at least once for lentic, lotic, and wetland habitat types, respectively. The climatic gap analysis indicated coverage is high for temperate regions but lacking in the tropics and Arctic, particularly for wetland ecosystems. Our assessment revealed high climatic coverage of freshwater microbial diversity knowledge, but expansive ecoregional gaps attributable to biased sampling near research institutions in North America, western Europe, and China. Future surveys should target ecoregions in Africa, South America, Central Asia, Australia, and Antarctica. An essential next step will be to curate and disseminate sequencing efforts to facilitate the study of processes driving global diversity patterns.

16S rRNA↗
DOE OSTI2022

ODI notebook additive_manufacturing_video_2022

Additive Manufacturing, video dataset Two-photon lithography (TPL) is a widely used 3D nanoprinting technique that uses laser light to create objects. Challenges to large-scale adoption of this additive manufacturing method include identifying light dosage parameters and monitoring during fabrication. A research team from LLNL, Iowa State University, and Georgia Tech is applying machine learning models to tackle these challenges-i.e., accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. Funded by LLNL's Laboratory Directed Research and Development Program, the project team has curated a video dataset of TPL processes for parameters such as light dosages, photo-curable resins, and structures. Both raw and labeled versions of the datasets are available on the links in the Open Data Initiative page. The code uses the labeled dataset. Notebook compiled by Nisha Mulakken (mulakken1@llnl.gov) for LLNL Open Data Initiative, Summer 2022. Original code provided by research team. Publications: X.Y. Lee, S.K. Saha, S. Sarkar, B. Giera. "Automated detection of part quality during two-photon lithography via deep learning." Additive Manufacturing 36, December 2020: doi.org/10.1016/j.addma.2020.101444 X.Y. Lee, S.K. Saha, S. Sarkar, B. Giera. "wo Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures." Data in Brief 32, October 2020. doi.org/10.1016/j.dib.2020.106119.

Mulakken, NishaJ↗
DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Delayed Neutron Temporal Signatures for Uranium Enrichment Measurement NA-241 SGTech (Final Report)

Nondestructive determination of uranium enrichment is a core capability for nuclear material accounting and control (NMAC) and safeguards verification measurements; however, traditional gamma spectroscopy-based techniques for enrichment measurement rely on significant assumptions of material composition and geometry, precluding their use in scenarios where a heterogeneous spatial distribution of enrichments is encountered. As an alternative, we are developing a technique to use delayed neutron temporal signatures for the measurement of uranium enrichment. Each uranium isotope has unique delayed neutron group yields, resulting in a unique delayed neutron decay time profile which can be analyzed to determine enrichment without the need for calibration sources. As part of this effort, we performed a series of measurement campaigns in which we used an active well coincidence counter (AWCC) retrofitted with commercial D-D and D-T generators to evaluate the operational characteristics of this method in response to a set of uranium enrichment and mass standards, as well as representative diversion scenarios in which either “concealed” enriched uranium is shielded by depleted uranium or declared enriched uranium is “hollowed out” and replaced with a central region of depleted uranium. A standard operating procedure and best practices were compiled to facilitate the use of delayed neutron-based enrichment measurements for international safeguards inspections.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Towards reverse mode automatic differentiation of Kokkos-based codes

Derivative computation is a key component of optimization, sensitivity analysis, uncertainty quantification, and the solving of nonlinear problems. Automatic differentiation (AD) is a powerful technique for evaluating such derivatives, and in recent years, has been integrated into programming environments such as Jax, PyTorch, and TensorFlow to support derivative computations needed for training of machine learning models, facilitating wide-spread use of these technologies. The C++ language has become the de facto standard for scientific computing due to numerous factors, yet language complexity has made the wide-spread adoption of AD technologies for C++ difficult, hampering the incorporation of powerful differentiable programming approaches into C++ scientific simulations. This is exacerbated by the increasing emergence of architectures, such as GPUs, with limited memory capabilities and requiring massive thread-level concurrency. C++ AD tools must effectively use these environments to bring novel scientific simulations to next-generation DOE experimental and observational facilities. In this project, we investigated source transformation-based automatic differentiation using LLVM compiler infrastructure to automatically generate portable and efficient gradient computations of Kokkos-based code. We have demonstrated that our proposed strategy is feasible by investigating the usage of a prototype LLVM-based source transformation tool to generate gradients of simple functions made of sequences of simple Kokkos parallel regions. Speedups of up to 500x compared to Sacado were observed on NVIDIA V100 GPU.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Advanced defrosting techniques in air source heat pumps: A review of vapor injection, thermal energy storage, and experimental frost accumulation data

Electrification is a critical step for reducing greenhouse gas emissions from heating. Air source heat pumps (ASHPs) are a promising alternative to fossil fuel-based systems due to their high coefficients of performance (COP), dual heating and cooling capability, and lower carbon footprint. However, for ASHPs to achieve widespread adoption, they must operate reliably across all climates, including cold regions. Additionally, defrosting techniques should be energy efficient and minimally disruptive to indoor comfort. Vapor injection (VI) technology can address the high-pressure and high-temperature lift challenges encountered in low ambient conditions. More recently, in addition to enhancing heating performance, VI has also been shown to improve the speed and efficiency of reverse cycle defrosting. Likewise, thermal energy storage (TES) has steadily gained attention for its ability to serve as an auxiliary heat source during both normal operation and defrosting. This review analyzes the benefits and limitations of VI- and TES-assisted defrosting approaches. While both technologies show strong potential individually, no studies to date have explored their combined use in ASHP systems. Additionally, to support continued development of defrosting strategies, both in modeling and experimental work, it is critical to establish frost accumulation data under a range of operating conditions. By compiling the available data from the literature, this paper also highlights the limited availability of such experimental data and the wide variation in frosting and defrosting durations and termination criteria, which are often influenced by system design and test setups.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A Systematic Evidence Based Performance Approach to Regulation of Nuclear Sites in England and Wales - 20058

The Environment Agency for England has developed a systematic evidence-based approach to pursue our strategic environmental objectives for the regulation of nuclear sites. We use an annual evidence review process to ensure effective and efficient targeting of limited resources to achieve those objectives. Over the last 8 years, we have been developing and refining this approach to ensure risk-based and value driven regulation. The approach comprises nuclear site and nuclear sector review processes known as Site Environment Review (SER) and Nuclear Environment Review (NER). This approach complements our regulation of nuclear site permit holders under the Environmental Permitting Regulations (EPR). We deliver our regulation of nuclear sites in England and Wales alongside the Office for Nuclear Regulation. The SER process involves the lead regulator for each nuclear site assessing the permit holder's environmental performance across 14 themes, set within the context of the site's main activities and associated waste disposals. Our themes include environmental leadership, resources and climate change, radioactive waste management, facility management and decommissioning, groundwater, and environmental radiological protection. We use evidence from site inspections and working within our subject matter groups to grade current and predicted future environmental performance. We are particularly interested in sustainability and the application of Best Available Techniques (BAT) to prevent the creation, and minimise the disposal, of radioactive wastes. We use risk analysis (strengths, weaknesses, threats and opportunities) to examine performance against our strategic environmental objectives, which are set out, in our 5-year Nuclear Delivery Plan (NDP). The output supports the targeting of our resources at each nuclear site. We consult the relevant permit holders on the SER priorities and use their feedback to refine our plans. We expect all permit holders to take account of our priorities when considering their own programmes of work, objectives and plans. The NER process brings together what we learn and achieve through regulation across the sector. It provides input to planning priorities, supported by qualitative and semi-quantitative evidence. It covers the 28 nuclear sites in England and Wales and spans the same 14 environmental themes. During the process we collate, compile and summarise evidence from the SERs and other sources such as inspection reports and evidence from our other nuclear work programmes. The output is the NER annual report. This provides a snapshot of the status of the nuclear sector and gives insights to enable us to regulate more efficiently and effectively. It also takes account of cross-cutting issues and risks such as changes in international standards, domestic policy, regulatory framework, domestic standards and guidance, learning from experience such as incidents, events and good practice, and innovation, research and development. It provides graphics that illustrate the grading of environmental performance for the nuclear sector across the fourteen environmental themes. This analysis allows benchmarking of nuclear site's environmental performance and the visualisation provides a convenient comparison of performance across themes, sites, and over time. We use this intelligence to inform our investment in training and development of our staff, and our cross-cutting engagement on strategic issues with government, the Nuclear Decommissioning Authority (NDA) and other corporate organisations. Adopting this approach can provide benefits with organisational reputation, stakeholder participation and ensuring value from the public investment. This paper describes the history of the SER/NER process, a selection of outputs from the process and ideas for improvement. The paper will be of interest to other regulators and organisations across the world that are interested in supporting continuous improvement. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Neural Network Analysis of Nuclear Magnetic Resonance and Infrared Spectra

Nuclear magnetic resonance (NMR) spectroscopy and infrared (IR) spectroscopy are powerful chemical characterization techniques with broad general usage. However, the manual evaluation of the resulting spectra is time-consuming and requires significant expertise, preventing insights from being used in real-time applications. With recent advances in computation and artificial intelligence (AI), new tools are available for automating spectral interpretation. In this work, machine learning (ML) algorithms using 1-dimensional convolutional neural networks (CNNs) were applied to identify common functional groups from spectral information. Raw spectra were collected virtually from the Human Metabolome Database (HMDB) and National Institute of Standards and Technology (NIST) Chemistry WebBook and processed into a suitable standard. Algorithm design was tailored to best fit the nature of the problem, with built-in flexibility to accommodate relevant parameters beyond the raw spectral input, specifically solvent identity and magnetic frequency for NMR. The predictive capability of the algorithm in identifying functional groups is displayed in several examples. This methodology has been compiled into a code repository and could easily be modified to adapt alternative data sources, including other spectrum types. To mitigate overfitting, a common problem in mathematical modeling where overfamiliarity with training data produces trends that are not representative of the general data, a novel metric was developed, referred to as Accufit. Accufit includes a parameter that penalizes substantial differences in the training accuracy and the accuracy of an independent validation set. Examples are presented showing the effectiveness of Accufit in maintaining the model’s predictive capability while controlling the overfitting when used as a custom metric for hyperparameter tuning.

Sturgill, James↗
DOE OSTI2025

A time-parallel multiple-shooting method for large-scale quantum optimal control

Quantum optimal control plays a crucial role in quantum computing by providing the interface between compiler and hardware. Solving the optimal control problem is particularly challenging for multi-qubit gates, due to the exponential growth in computational complexity with the system's dimensionality and the deterioration of optimization convergence. To ameliorate the computational complexity of time-integration, this paper introduces a multiple-shooting approach in which the time domain is divided into multiple windows and the intermediate states at window boundaries are treated as additional optimization variables. Further, this enables parallel computation of state evolution across time-windows, significantly accelerating objective function and gradient evaluations. Since the initial state matrix in each window is only guaranteed to be unitary upon convergence of the optimization algorithm, the conventional gate trace infidelity is replaced by a generalized infidelity that is convex for non-unitary state matrices. Continuity of the state across window boundaries is enforced by equality constraints. A quadratic penalty optimization method is used to solve the constrained optimal control problem, and an efficient adjoint technique is employed to calculate the gradients in each iteration. We demonstrate the effectiveness of the proposed method through numerical experiments on quantum Fourier transform gates in systems with 2, 3, and 4 qubits, noting a speedup of 80x for evaluating the gradient in the 4-qubit case, highlighting the method's potential for optimizing control pulses in multi-qubit quantum systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

The Essence of Cryptol: A Denotational Cryptol Interpreter in Coq for Foundational Assurances for Quantum Resistant Cryptosystems

Systems of the utmost consequence need a means to establish authenticity of software and data. Cryptosystems implement authentication, but can be vulnerable to cryptographic and implementation attacks. With the threat of quantum cryptographic attacks, “post-quantum” cryptosystems (PQCs) must be henceforth used in these systems. However, the new cryptography needs new ways to, rigorously and machine-checkably, prove systems free of vulnerabilities. We propose a retargetable capability to rapidly instantiate proven correct postquantum cryptosystems through novel proof-carrying synthesis and proof-automation technique, extending those proven successful on existing systems. This capability is crucial to meeting the cryptographic requirements for future high-consequence systems. Since specifications for high consequence cryptography are presently captured in a domain specific language known as Cryptol. While this can enable convenient fully automated reasoning about Cryptol specificaitons and implementations via the Software Analysis Workbench (SAW), Cryptol has expressivity gaps, so that cryptosystems with probabilistic programming features like Falcon cannot be fully expressed in the language. Moreover, SAW’s automation fails for programs and specificaitons with inductive and recursive structure, as in the Sphincs+ PQC. Finally, Cryptol and SAW together represent some 200,000 lines of unverified Haskell, so that the any guarantees about high consequence cryptography are presently contingent on a large, unverified, yet trusted computing base. The first step of the larger project of agile, assured crpytography is therefore to provide a formal, mechanized semantics for Cryptol, so that the specifications expressed by cryptographers in Cryptol can be reasoned about and compiled into performant implementations with a foundational, machine checkable certificate of correctness. This report describes our work on this first step, culminating in the design of a certified denotational interpreter, in Coq, for core Cryptol.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

MRCI Task 3: Facilitating Data Collection, Sharing, and Analysis Final Technical Summary Report

The Midwest Regional Carbon Initiative (MRCI) Task 3.0 was defined to facilitate development of carbon capture, utilization, and storage (CCUS) in the region by collection and sharing of existing and new technical data from CCUS projects and research. The task also included support for further analysis and assessment of tools by the project team and by researchers working on programs such as National Risk Assessment Partnership (NRAP), machine learning (ML) techniques, and assessment and improvement of CCUS site assessment, operations, and monitoring aspects. Work under Task 3.0 addressed key issues related to CCUS deployment and provided foundational research and datasets to help establish CCUS projects in the MRCI. Report Authors and Principal Technical Contributors: Joel Sminchak, Laura Keister, Mackenzie Scharenberg, Priya Ravi-Ganesh, Autumn Haagsma, Srikanta Mishra, Jared Hawkins, Jared Schuetter, Amy Lang, Jaelen Lewis, Derrick James, Jorge Barrios, Stuart Skopec, and Sanjay Mawalkar (Battelle). Chris Korose, Carl Carmen, Nate Grigsby, Nathan Webb (Illinois State Geological Survey). Principal Investigators: Dr Neeraj Gupta, Dr. Chris Korose.

MRCI,NRAP,data collection,data compilation,legacy ↗
DOE OSTI2024

Cross sections for the formation of Rb84m,g, Rb83, and Rb82m in Sr86(d,x) reactions up to deuteron energies of 49 MeV: Competition between α-particle and multinucleon emission processes

Cross sections of Sr86(d,x) reactions leading to the products Rb84m,g, Rb83, and Rb82m were measured by the stacked-sample activation technique up to deuteron energies of 49 MeV. Nuclear model calculations were performed using the codes talys and empire, which combine the statistical, precompound, and direct interaction components. In all cases, the empire results were much higher than the talys calculation. Fairly good agreement was obtained between measured data and the talys calculation after some optimization of the input model parameters. Insight into competition between α-particle and multinucleon emission in the Y88 compound-nucleus system was also gained.

59 ≤ A ≤ 89↗
DOE OSTI2025

Postdoctoral insights on mentoring excellence: a framework for best practices at Sandia National labs

The Sandia National Laboratories Strategic Plan FY24-FY27, updated for FY25, outlines Sandia’s two Big Labs-wide Goals, Accelerate Innovation and Lead in Modern Engineering. The goal of Accelerate Innovation is that “by FY27, Sandia will be a leader in scientific, engineering and operational innovation and an employer of choice for highly innovative and creative talent.” Sandia’s postdocs are leaders in innovation, well-versed in emerging techniques and cutting-edge methods, and capable of acting as a highly agile technical force across domains at the lab. As a federally funded research and development center (FFRDC), Sandia National Laboratories attracts top doctoral talent by offering a unique opportunity for postdoctoral researchers to develop at the crossroads of government, academia, and industry, working in multi-disciplinary teams and performing cutting-edge, mission-specific research that responds to immediate needs of national interest. However, this creates unique opportunities and demands of both postdoctoral appointees and the Sandia staff who act as their mentors, making mentorship key to attract talent. Since 2007 the Sandia Postdoctoral Development (SPD) Board, originally Postdoc To Professional (PD2P), a networking group at Sandia composed of a voluntary board of current postdocs and two staff liaisons, has advocated for postdoctoral development within Sandia National Labs. In this white paper, SPD board members and the Sandia Postdoctoral Development Office, organized in 2019, have come together to develop a comprehensive overview of the postdoctoral mentoring landscape at Sandia National Labs as we currently know it. By scouring various forms of data from efforts since 2018, we’ve compiled a community-derived perspective on what makes postdoctoral mentorship at Sandia unique. First, we analyze working sessions held between mentors and mentees to develop a comprehensive map of who is involved in postdoctoral mentorship at the lab and how the responsibilities are divided amongst mentors and mentees. We then combine multiple forms of data, including exit surveys, annual surveys, and community workshops, to identify the specific challenges that mentors and mentees encounter at the national lab. Finally, we use text mining and sentiment analysis to analyze mentoring award data to develop an idea of what postdocs are self-identifying as excellent mentorship within the lab. It is our goal that this white paper act as an ongoing resource to the postdoc and postdoc mentoring communities and provide a firm foundation for further conversations on the future of postdoctoral mentorship at Sandia National Labs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Dust Reverberation Mapping in Distant Quasars from Optical and Mid-infrared Imaging Surveys

The size of the dust torus in active galactic nuclei (AGNs) and their high-luminosity counterparts, quasars, can be inferred from the time delay between UV/optical accretion disk continuum variability and the response in the mid-infrared (MIR) torus emission. This dust reverberation mapping (RM) technique has been successfully applied to ~70 z ≲ 0.3 AGNs and quasars. Here we present first results of our dust RM program for distant quasars covered in the Sloan Digital Sky Survey Stripe 82 region combining ~20 yr ground-based optical light curves with 10 yr MIR light curves from the WISE satellite. Here, we measure a high-fidelity lag between W1 band (3.4 μm) and g band for 587 quasars over 0.3 ≲ z ≲ 2 () and two orders of magnitude in quasar luminosity. They tightly follow (intrinsic scatter ~0.17 dex in lag) the IR lag–luminosity relation observed for z < 0.3 AGNs, revealing a remarkable size–luminosity relation for the dust torus over more than four decades in AGN luminosity, with little dependence on additional quasar properties such as Eddington ratio and variability amplitude. This study motivates further investigations in the utility of dust RM for cosmology and strongly endorses a compelling science case for the combined 10 yr Vera C. Rubin Observatory Legacy Survey of Space and Time (optical) and 5 yr Nancy Grace Roman Space Telescope 2 μm light curves in a deep survey for low-redshift AGN dust RM with much lower luminosities and shorter, measurable IR lags. The compiled optical and MIR light curves for 7384 quasars in our parent sample are made public with this work.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Cross-Feature Transfer Learning for Efficient Tensor Program Generation

Tuning tensor program generation involves navigating a vast search space to find optimal program transformations and measurements for a program on the target hardware. The complexity of this process is further amplified by the exponential combinations of transformations, especially in heterogeneous environments. This research addresses these challenges by introducing a novel approach that learns the joint neural network and hardware features space, facilitating knowledge transfer to new, unseen target hardware. A comprehensive analysis is conducted on the existing state-of-the-art dataset, TenSet, including a thorough examination of test split strategies and the proposal of methodologies for dataset pruning. Leveraging an attention-inspired technique, we tailor the tuning of tensor programs to embed both neural network and hardware-specific features. Notably, our approach substantially reduces the dataset size by up to 53% compared to the baseline without compromising Pairwise Comparison Accuracy (PCA). Furthermore, our proposed methodology demonstrates competitive or improved mean inference times with only 25–40% of the baseline tuning time across various networks and target hardware. The attention-based tuner can effectively utilize schedules learned from previous hardware program measurements to optimize tensor program tuning on previously unseen hardware, achieving a top-5 accuracy exceeding 90%. This research introduces a significant advancement in autotuning tensor program generation, addressing the complexities associated with heterogeneous environments and showcasing promising results regarding efficiency and accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Extraction of ground-state nuclear deformations from ultrarelativistic heavy-ion collisions: Nuclear structure physics context

The collective-flow-assisted nuclear shape-imaging method in ultrarelativistic heavy-ion collisions (UHICs) has recently been used to characterize nuclear collective states. In this paper, we assess the foundations of the shape-imaging technique employed in these studies. We argue that some current UHIC nuclear imaging techniques neglect fundamental aspects of spontaneous symmetry breaking and symmetry restoration in colliding ions and incorrectly infer one-body multipole moments from studies of nucleonic correlations. Therefore, the impact of this approach on nuclear structure research has been overstated. Conversely, efforts to incorporate existing knowledge on nuclear shapes into analysis pipelines can be beneficial for benchmarking tools and calibrating models used to extract information from ultrarelativistic heavy-ion experiments.

Nuclear data analysis & compilation↗
DOE OSTI2021

Precambrian Crystalline Basement Properties From Pressure History Matching and Implications for Induced Seismicity in the US Midcontinent

Injection-induced seismicity across the US midcontinent has almost exclusively occurred in the crystalline basement that underlies the Arbuckle Group aquifer and its equivalents, the primary wastewater disposal zone in this region. However, the properties of the basement are not well known. Newly compiled data, from Class I wells in Kansas, provide a unique record of pressures in the Arbuckle and an opportunity to constrain the reservoir-scale properties of the basement such as permeability, diffusivity, and specific storage. Constraints on these parameters are critical for modeling fluid flow and pressures across the entire Arbuckle-basement system, and are necessary for accurate evaluation and prediction of injection-induced earthquakes. Here, we present a detailed, three-dimensional geological and pressure history-matched numerical model for the Arbuckle and basement, based on data from >400 wells covering a large region in south-central Kansas, where injection-induced seismicity has been concentrated since 2014. Simulations of dynamic data from 319 wells indicate that Arbuckle pressures have increased by 1.1 MPa in high injection rate areas and an overpressure of <0.1 MPa may be the cause of seismicity in the basement. Pressure-history matching also yields the likely range in porosity (0.3%–7%), permeability (0.1–0.7 mD), and diffusivity (0.004–0.07 m2 /s) for the basement. The resulting estimates suggest reservoir-scale properties of the basement are enhanced by faults and fractures. Importantly, the diffusivities determined in this study are lower than estimates derived from Kansas earthquake triggering fronts, and suggest that such seismicity-based techniques may have limitations, particularly where spacetime patterns between injection and seismicity are complex.

58 GEOSCIENCES↗