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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation

Purpose: Nonstationarity is an important aspect of imaging performance in CT and cone-beam CT (CBCT), especially for systems employing iterative reconstruction. This work presents a theoretical framework for both filtered-backprojection (FBP) and penalized-likelihood (PL) reconstruction that includes explicit descriptions of nonstationary noise, spatial resolution, and task-based detectability index. Potential utility of the model was demonstrated in the optimal selection of regularization parameters in PL reconstruction. Methods: Analytical models for local modulation transfer function (MTF) and noise-power spectrum (NPS) were investigated for both FBP and PL reconstruction, including explicit dependence on the object and spatial location. For FBP, a cascaded systems analysis framework was adapted to account for nonstationarity by separately calculating fluence and system gains for each ray passing through any given voxel. For PL, the point-spread function and covariance were derived using the implicit function theorem and first-order Taylor expansion according toFessler [“Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography,” IEEE Trans. Image Process. 5(3), 493–506 (1996)]. Detectability index was calculated for a variety of simple tasks. The model for PL was used in selecting the regularization strength parameter to optimize task-based performance, with both a constant and a spatially varying regularization map. Results: Theoretical models of FBP and PL were validated in 2D simulated fan-beam data and found to yield accurate predictions of local MTF and NPS as a function of the object and the spatial location. The NPS for both FBP and PL exhibit similar anisotropic nature depending on the pathlength (and therefore, the object and spatial location within the object) traversed by each ray, with the PL NPS experiencing greater smoothing along directions with higher noise. The MTF of FBP is isotropic and independent of location to a first order approximation, whereas the MTF of PL is anisotropic in a manner complementary to the NPS. Task-based detectability demonstrates dependence on the task, object, spatial location, and smoothing parameters. A spatially varying regularization “map” designed from locally optimal regularization can improve overall detectability beyond that achievable with the commonly used constant regularization parameter. Conclusions: Analytical models for task-based FBP and PL reconstruction are predictive of nonstationary noise and resolution characteristics, providing a valuable framework for understanding and optimizing system performance in CT and CBCT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Biomass Electrochemical Reactor for Upgrading Biorefinery Waste to Industrial Chemicals and Hydrogen

The goal of this project was to develop an electrochemical process for depolymerization of biorefinery lignin to industrial chemicals, most notably as replacements in resins. The project team consisted of Ohio University (lead organization), Hexion, Inc. and Lakehead University. There were six distinct project tasks, consisting of Initial Validation, Development of TiO 2 Nanowire-supported Ni-Co Electrocatalysts, Electrochemical Characterization of TiO 2 Nanowire-supported Ni-Co Electrocatalysts, Development of Electrochemical Flow Reactor for Production of Industrial Chemicals and Hydrogen, Product Formulations and Integrated Biorefinery Analysis. The project was divided into two budget periods. After intermediate validation near the end of Budget Period 1, the project was discontinued. This final report is intended to highlight major project objectives and results. Technical metrics were developed between the project team and the DOE validation team during the initial validation period. Critical technical metrics included conversion of lignin to products and product selectivity. Desired products were low molecular weight aromatic compounds in the 150-300 MW range. We targeted this class of compounds because of their potential to replace petroleum derivatives in resin formulations. Electrochemical oxidation of biorefinery lignin can lead to significant lignin depolymerization and conversion, with >40% of the lignin reacted. However, product stream analysis is extremely difficult. We were not able to adequately quantify the concentration of the primary oxidation products, or to determine with any degree of certainty what the product selectivity was. The project team initially proposed analytical techniques such as GC-MS to identify product distributions. However, due to 1) difficulty in extracting products into an organic phase suitable for GC analysis and 2) the wide range of product compounds with similar structures, GC-MS analysis was difficult to perform. The project team did have some success with NMR spectroscopic analysis, but not enough so to adequately identify product selectivity. By UV-vis spectroscopy and statistical analysis, we were able to confirm achieving the technical milestone for lignin conversion, and achieved at least 40% conversion by the electrochemical oxidation technique. However, we were not able to confirm product selectivity using GC-MS analysis, as originally postulated. Despite these difficulties, we were able to incorporate the product stream into a resin, and observed better resin synthesis behavior using the oxidized lignin versus the unoxidized lignin. However, no resin synthesized with oxidized biorefinery lignin met the commercial standards for resin quality. One potential problem with electrochemical oxidation of biomass is the competing oxygen evolution reaction (OER) at the reactor anode. We observed that, especially at higher anode potentials, the OER can consume up to 50% of the energy input to the system, meaning that much of the energy goes toward generation of an essentially worthless product (O 2 ). However, we also observed that the OER can be avoided almost entirely at lower potentials. These uncertainties made assigning a value to the electrochemical reactor product stream difficult. Any product generated electrochemically would have to be valuable enough to offset the additional cost of electricity incurred, including electricity to run the electrochemical reactor and the additional electricity that must be purchased because some of the lignin (which is currently burned in biorefineries to recover energy) is diverted to the electrochemical reactor. It is not clear at this time whether the product stream has sufficient economic value to offset these additional electricity costs. The two most promising areas for this project in terms of demonstrating economic feasibility are product stream analysis and resin synthesis. In particular, product stream analysis has proven to be difficult. With additional time, we would refine analytical techniques to better understand key product stream characteristics, such as aromatic compound content. Evaluating the product streams in resin synthesis would likely provide more information on the economic value of the product stream. If we could determine that value, then we would be able to better evaluate whether the electrochemical conversion approach is feasible. The details of this work are presented in the following final report. The report is organized into individual tasks for clarity, although all tasks were integrated and many occurred simultaneously.

09 BIOMASS FUELS↗
DOE OSTI2022

Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned

Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e.g., law, healthcare, education, etc. However, only limited efforts have investigated the opportunities and limitations of applying these powerful models to science and security applications. In this work we develop foundation models of scientific knowledge for chemistry to augment scientists with the advanced ability to perceive and reason at scale previously unimagined. Specifically, we build large-scale (1.47B parameter) general-purpose models for chemistry that can be effectively used to perform a wide range of in-domain and out-of-domain tasks. Evaluating these models in a zero-shot setting, we analyze the effect of model and data scaling, knowledge depth, and temporality on model performance in context of model training efficiency. Our novel findings demonstrate that (1) model size significantly contributes to the task performance when evaluated in a zero-shot setting; (2) data quality (aka diversity) affects model performance more than data quantity; (3) similarly, unlike previous work (Luu et al., 2021) temporal order of the documents in the corpus boosts model performance only for specific tasks, e.g., SciQ; and (4) models pre-trained from scratch perform better on in-domain tasks than those tuned from general-purpose models like Open AI’s GPT-2.

Foundation Models, Chemistry↗
DOE OSTI2020

Enhanced Recovery Opportunities in the Appalachian Basin

The Midwest Regional Carbon Sequestration Partnership (MRCSP) has incorporated the work of geologic research teams (Geoteams) in its regional characterization, project planning and carbon dioxide (CO2) injection implementation work since the partnership was established by the U.S. Department of Energy (DOE) in 2003. Over this 16-year period, the cohort of Geoteams has grown from five to ten states and has contributed to the characterization of geologic sequestration opportunities, refinement of reservoir and seal data, and supported injection efforts through both predictive and post-injection assessments. The regional characterization work conducted by the Geoteams during the MRCSP Phase III project period (2010 – 2019) focused on the following tasks: (1) refinement of geologic seals/reservoir systems; (2) assessment of Atlantic Coastal Plain and offshore opportunities; (3) expanded assessments of oil and gas fields, particularly as they relate to enhanced recovery opportunities; (4) regional support for implementation of carbon capture utilization and storage (CCUS) in the partnership area; and (5) communication and data sharing. The findings of this work are summarized in the final report entitled Final Report of Geologic Carbon Capture Utilization and Storage Opportunities in the form of a state-by-state presentation for the MRCSP Region. In addition to the capstone deliverable mentioned above, the Geoteams have also prepared a series of topical reports to elaborate on specific geologic intervals and/or geographic areas of study completed during the Phase III project period. Specifically, these topical reports address: (1) the Atlantic Coastal Plain and adjacent offshore; (2) Cambro-Ordovician reservoirs/seals in the region; (3) enhanced oil and gas recovery opportunities in the Appalachian Basin; and (4) enhanced oil recovery (EOR) in the Michigan Basin. The remainder of this topical report presents our findings relative to enhanced recovery opportunities in the Appalachian Basin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Online Produced Water Treatment Catalog and Decision Tool

The objective of this project was to create an internet-based Water Treatment Technology Catalog and Decision Tool that will increase production, decrease costs and enhance environmental protection. This is to be accomplished by pairing an operator's water treatment cost and capacity needs to specific water treatments. This project cataloged existing and emerging produced water treatment technologies and allows operators to identify the most cost-effective approaches for managing their produced water. The tool captures the cost and capabilities of each technology and the disposal and beneficial use options for each region. The tool then takes location, chemical composition, and volumetric data for the operator's water and identifies the most cost effective treatment options for that water. Regulatory requirements or limitations for each location are also addressed. The Produced Water Treatment Catalog and Decision Tool efficiently matches industry decision makers in unconventional natural gas basins with: 1) appropriate and applicable water treatment technologies for their project, 2) relevant information on regulatory and legal issues that may impact the success of their project, and 3) potential beneficial use demands specific to their project area. To ensure the success of this project, it was segmented into seven tasks conducted in three phases over a three year period. The tasks were overseen by a Project Advisory Council (PAC) made up of stakeholders including state and federal agency representatives and industry representatives. ALL Consulting has made the catalog and decision tool available on the Internet for the final year of the project. The second quarter of the second budget period, work was halted based on the February 18, 2011 budget availability; however previous project deliverables were submitted on time and the deliverables for Task 6 and 7 were completed ahead of schedule. Thus the application and catalog were deployed to the public Internet. NETL did not provide additional funds and work on the project stopped on February 18, 2011. NETL ended the project on March 31, 2012.

03 NATURAL GAS↗
DOE OSTI2023

Biofuels Information Center

The purpose of the Biofuels Information Center (BIC) task is to provide relevant data, information, reports, and web-based tools to all bioenergy stakeholders. The BIC task began in FY08 to meet the requirement under Title II, Sec. 229 of the Energy Independence and Security Act of 2007 (EISA) requires DOE to develop a "Biofuels and Biorefinery Information Center". The BIC task supports biofuels pages content on the EERE's most visited website - the Alternative Fuels Data Center (AFDC http://www.afdc.energy.gov) and the Bioenergy Atlas tools (currently archived) (previous address https://maps.nrel.gov). This task results in more than 1.7 million web pageviews (an instance of an internet user visiting a webpage) per year. In FY22, the task completed the final year of the 5 year USDA Biofuels Infrastructure Partnership (BIP). The USDA BIP expanded infrastructure for E15 and/or E85 to approximately 850 stations and NREL received and reviewed data for quality analyzed all infrastructure and sales data collected by USDA. Stations are privately held and previously it was difficult to ascertain infrastructure and sales data. This unique dataset allows insight into infrastructure data (number of pumps and tanks, costs to install new equipment) and sales data (price and volume for E10, E15, E85, and diesel by month). The 2021 USDA BIP National Summary Report is with DOE for review prior to publication. Future work will include the biannual Bioenergy Industry Status Report (4 previous versions have been published) . The task also supports the principal investigator's time to engage stakeholders on infrastructure and deployment of biofuels. This includes leading, membership, and participation in the following roles: member Board of Advisors at the Fuels Institute, voting member for multiple UL standards committees, Co-Chair of the Infrastructure team at Agriculture/Auto/Ethanol, Member of Coordination Research Council's ULSD Corrosion Committee. The Principal investigator routinely responds to industry inquires to assist in deployment of biofuels regularly.

biofuels↗
DOE OSTI2011

Assessing Risk and Driving Risk Mitigation for First-of-a-Kind Advanced Reactors

Planning and decision making amidst programmatic and technological risks represent significant challenges for projects. This presentation addresses the four step risk-assessment process needed to determine clear path forward to mature needed technology and design, license, and construct advanced nuclear power plants, which have never been built before, including Small Modular Reactors. This four step process has been carefully applied to the Next Generation Nuclear Plant. STEP 1 - Risk Identification Risks are identified, collected, and categorized as technical risks, programmatic risks, and project risks, each of which result in cost and schedule impacts if realized. These include risks arising from the use of technologies not previously demonstrated in a relevant application. These risks include normal and accident scenarios which the SMR could experience including events that cause the disablement of engineered safety features (typically documented in Phenomena Identification Ranking Tables (PIRT) as produced with the Nuclear Regulatory Commission) and design needs which must be addressed to further detail the design. Product - Project Risk Register contained in a database with sorting, presentation, rollup, risk work off functionality similar to the NGNP Risk Management System . STEP 2 - Risk Quantification The risks contained in the risk register are then scored for probability of occurrence and severity of consequence, if realized. Here the scoring methodology is established and the basis for the scoring is well documented. Product - Quantified project risk register with documented basis for scoring. STEP 3 - Risk Handling Strategy Risks are mitigated by applying a systematic approach to maturing the technology through Research and Development, modeling, test, and design. A Technology Readiness Assessment is performed to determine baseline Technology Readiness Levels (TRL). Tasks needed to mature the technology are developed and documented in a roadmap. Product - Risk Handling Strategy. STEP 4 - Residual Risk Work off The risk handling strategy is entered into the Project Risk Allocation Tool (PRAT) to analyze each task for its ability to reduce risk. The result is risk-informed task prioritization. The risk handling strategy is captured in the Risk Management System, a relational database that provides conventional database utility, including data maintenance, archiving, configuration control, and query ability. The tool's Hierarchy Tree allows visualization and analyses of complex relationships between risks, risk mitigation tasks, design needs, and PIRTs. Product - Project Risk Allocation Tool and Risk Management System which depict project plan to reduce risk and current progress in doing so.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Learning to Rank in the Age of Muppets: Effectiveness–Efficiency Tradeoffs in Multi-Stage Ranking

It is well known that rerankers built on pretrained transformer models such as BERT have dramatically improved retrieval effectiveness in many tasks. However, these gains have come at substantial costs in terms of efficiency, as noted by many researchers. In this work, we show that it is possible to retain the benefits of transformer-based rerankers in a multi-stage reranking pipeline by first using feature-based learning-to-rank techniques to reduce the number of candidate documents under consideration without adversely affecting their quality in terms of recall. Applied to the MS MARCO passage and document ranking tasks, we are able to achieve the same level of effectiveness, but with up to 18 increase in efficiency. Furthermore, our techniques are orthogonal to other methods focused on accelerating transformer inference, and thus can be combined for even greater efficiency gains. A higher-level message from our work is that, even though pretrained transformers dominate the modern IR landscape, there are still important roles for “traditional” LTR techniques, and that we should not forget history.

catalysis↗
DOE OSTI2024

Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids

Traditional analysis of highly distorted micro X‐ray diffraction (μ‐XRD) patterns from hydrothermal fluid environments is a time‐consuming process, often requiring substantial data preprocessing and labeled experimental data. Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. MTL models are trained to identify phase information in μ‐XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Machine learning at the edge to improve in-field safeguards inspections

Artificial intelligence (AI) and machine learning (ML) are near-ubiquitous in day-to-day life; from cars with automated driver-assistance, recommender systems, generative content platforms, and large language chatbots. Implementing AI as a tool for international safeguards could significantly decrease the burden on safeguards inspectors and nuclear facility operators. The use of AI would allow inspectors to complete their in-field activities quicker, while identifying patterns and anomalies and freeing inspectors to focus on the uniquely human component of inspections. Sandia National Laboratories has spent the past two and a half years developing on-device machine learning to develop both a digital and robotic assistant. This combined platform, which we term inspecta, has numerous on-device machine learning capabilities that have been demonstrated at the laboratory scale. Here this work describes early successes implementing AI/ML capabilities to reduce the burden of tedious inspector tasks such as seal examination, information recall, note taking, and more.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

DeepMerge: Classifying high-redshift merging galaxies with deep neural networks

In this work, we investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble Space Telescope; the data without noise form a "pristine" data set and that with noise form a "noisy" data set. The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy. The CNN outperforms a Random Forest classifier, which was shown to be superior to conventional one- or two-dimensional statistical methods (Concentration, Asymmetry, the Gini, $M_{20}$ statistics etc.), which are commonly used when classifying merging galaxies. We also investigate the selection effects of the classifier with respect to merger state and star formation rate, finding no bias. Finally, we extract Grad-CAMs (Gradient-weighted Class Activation Mapping) from the results to further assess and interrogate the fidelity of the classification model.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Extracting Material Property Measurements from Scientific Literature with Limited Annotations

Extracting material property data from scientific text is pivotal for advancing data-driven research in chemistry and materials science; however, the extensive annotation effort required to produce training data for named entity recognition (NER) models for this task often makes it a barrier to extracting specialized data sets. Here, in this work, we present a comparative study of the conventional, supervised NER methodology to alternative few-shot learning architectures and large language model (LLM)-based approaches that mitigate the need to label large training data sets. We find that the best-performing LLM (GPT-4o) not only excels in directly extracting relevant material properties based on limited examples but also enhances supervised learning through data augmentation. We supplement our findings with error and data quality assessments to provide a nuanced understanding of factors that impact property measurement extraction.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Dynamically Reconfigurable Metadevice Employing Nanostructured Phase-Change Materials

Mastering dynamic free-space spectral control and modulation in the near-infrared (NIR) and optical regimes remains a challenging task that is hindered by the available functional materials at high frequencies. In this work, we have realized an efficient metadevice capable of spectral control by minimizing the thermal mass of a vanadium dioxide phase-change material (PCM) and placing the PCM at the feed gap of a bow-tie field antenna. The device has an experimentally measured tuning range of up to 360 nm in the NIR and a modulation depth of 33% at the resonant wavelength. The metadevice is configured for integrated and local heating, leading to faster switching and more precise spatial control compared with devices based on phase-change thin films. We envisage that the combined advantages of this device will open new opportunities for signal processing, memory, security, and holography at optical frequencies.

42 ENGINEERING↗
DOE OSTI2017

Interconnected ionic domains enhance conductivity in microphase separated block copolymer electrolytes

Block copolymer electrolytes (BCEs) represent an attractive choice as solid-state ionic conductors for electrochemical technologies used in energy storage and conversion, water treatment, sensors, and data storage and processing. Unlocking the maximum ionic conductivity of BCEs requires an intimate understanding as to how the microphase separated structure influences transport properties. However, elucidating such knowledge remains elusive due to the challenging task of precisely engineering BCEs with a defined structure in bulk materials. In this work, we examined BCEs in a thin film format because it was amenable to attaining BCEs with a desired nanostructure. Specifically, we systematically investigated anion-conducting BCEs with different degrees of connectivity of the ionic domains. For the first time, we demonstrate that increasing terminal defects in the ionic domain from 1 terminal defect per mu m(2) to 20 terminal defects per mu m(2) ( a relatively small amount of defects) decreased ionic conductivity by 67% compared to the maximum value attained. Conversely, maximizing ionic domain connectivity increased the ionic conductivity by two-fold over a non-ordered BCE film. These experiments highlight that microphase separation alone was insufficient for ameliorating ionic conductivity in BCEs. Rather, microphase separation coupled with complete ionic domain connectivity realized BCEs with significantly enhanced ionic conductivity.

Arges, Christopher G. (ORCID:0000000317038323)↗
DOE OSTI2024

Uranyl uptake into metal–organic frameworks: a detailed X-ray structural analysis

Metal–organic frameworks (MOF) are a subclass of porous framework materials that have been used for a wide variety of applications in sensing, catalysis, and remediation. Among these myriad applications is their remarkable ability to capture substances in a variety of environments ranging from benign to extreme. Among the most common and problematic substances found throughout the world's oceans and water supplies is [UO 2 ] 2+ , a common mobile ion of uranium, which is found both naturally and as a result of anthropogenic activities, leading to problematic environmental contamination. While some MOFs possess high capability for the uptake of [UO 2 ] 2+ , many more of the thousands of MOFs and their modifications that have been produced over the years have yet to be studied for their ability to uptake [UO 2 ] 2+ . However, studying the thousands of MOFs and their modifications presents an incredibly difficult task. As such, a way to narrow down the numbers seems imperative. In this work, we evaluate the binding behaviors as well as identify the specific binding sites of [UO 2 ] 2+ incorporated into six different Zr MOFs to elucidate specific features that improve [UO 2 ] 2+ uptake. In doing so, we also present a method for the determination and verification of these binding sites by Anomalous wide-angle X-ray scattering, X-ray fluorescence, and X-ray absorption spectroscopy. This research not only presents a way for future research into the uptake of [UO 2 ] 2+ into MOFs to be conducted but also a means to evaluate MOFs more generally for the uptake of other compounds to be applied for environmental remediation and improvement of ecosystems globally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Space-time dependent thermal conductivity in nonlocal thermal transport

Nonlocal thermal transport is generally described by the Peierls-Boltzmann transport equation (PBE). However, solving the PBE for a general space-time dependent problem remains a challenging task due to the high dimensionality of the integro-differential equation. In this work, we present a direct solution to the space-time dependent PBE with a linearized collision matrix using an eigendecomposition method. We show that there exists a generalized Fourier-type relation that links heat flux to the local temperature, and this constitutive relation defines a thermal conductivity that depends on both time and space. Combining this approach with ab initio calculations of phonon properties, we demonstrate that the space-time dependent thermal conductivity gives rise to an oscillatory response in temperature in a transient grating geometry in high thermal conductivity materials. The present solution method allows us to extend the reach of our computational capability for heat conduction to space-time dependent nondiffusive transport regimes. Here, this capability will not only enable a more accurate interpretation of thermal measurements that observe nonlocal thermal transport, but also enhance our physical understanding of nonlocal thermal transport in high thermal conductivity materials that are promising candidates for nanoscale thermal management applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

IRIS-DMEM: Efficient Memory Management for Heterogeneous Computing

This paper proposes an efficient data memory management approach for the Intelligent RuntIme System (IRIS) heterogeneous computing framework along with new data transfer policies. IRIS provides a task-based programming model for extreme heterogeneous computing (e.g., CPU, GPU, DSP, FPGA) with support for today's most important programming languages (e.g., OpenMP, OpenCL, CUDA, HIP, OpenACC). However, the IRIS framework either forces the programmer to introduce data transfer commands for each task or relies on suboptimal memory management for automatic and transparent data transfers. The work described here extends IRIS with novel heterogeneous memory handling and introduces novel data transfer policies by employing the Distributed data MEMory handler (DMEM) for efficient and optimal movement of data among the various computing resources. The proposed approach achieves performance gains of up to 7× for tiled LU factorization and tiled DGEMM (i.e., matrix multiplication) benchmarks. Moreover, this approach also reduces data transfers by up to 71% when compared to previous IRIS heterogeneous memory management handlers. This work compares the performance results of the IRIS framework's novel DMEM with the StarPU runtime and MAGMA math library for GPUs. Experiments show a performance gain of up to 1.95× over StarPU and 2.1× over MAGMA.

Miniskar, Narasinga Rao↗
DOE OSTI2023

Lorentz group equivariant autoencoders

Abstract There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries. Such biases have been shown to make models more performant and interpretable, and reduce the amount of training data needed. To that end, we develop the Lorentz group autoencoder (LGAE), an autoencoder model equivariant with respect to the proper, orthochronous Lorentz group $$\textrm{SO}^+(3,1)$$ SO + ( 3 , 1 ) , with a latent space living in the representations of the group. We present our architecture and several experimental results on jets at the LHC and find it outperforms graph and convolutional neural network baseline models on several compression, reconstruction, and anomaly detection metrics. We also demonstrate the advantage of such an equivariant model in analyzing the latent space of the autoencoder, which can improve the explainability of potential anomalies discovered by such ML models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗