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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

The role of rifting in the generation of melt: Implications for the origin and evolution of the Lada Terra-Lavinia Planitia region of Venus

A rift over 6000 km in total length runs along the border of Lada Terra, a highland in the southern hemisphere of Venus, and Lavinia Planitia, a basin that has been interpreted as a site of early-stage mantle downwelling. Along the length of the rift are a number of volcanic centers of widely varying morphology and volcanic output. These include coronae, radially fractured domes, and large flow fields similar in scale to terrestrial flood basalts. We develop a model for the origin of extension related to passive rifting in response to stresses created by the adjacent downwelling. Volcanism and extension at other rifts on Venus, such as Devana Chasma, have been attributed to deep-seated mantle plume activity. In contrast, we interpret the origin of extension and volcanism along the Lada rift to be linked to upwelling and decompression melting of mantle material due to rifting and, possibly, to counterflow associated with downwelling. Extension occurred generally prior to the formation of volcanic centers and the eruption of large-scale flow fields, although most of the volcanic centers have been fractured by continued extension along the rift. Current debate over the formation of terrestrial flood basalts centers on the necessity of preexisting extension and stretched and thinned lithosphere to produce enhanced decompression melting within a large plume head or mantle thermal anomaly. Our studies of large-scale flow fields associated with the Lada rift and coronae on Venus indicate that extension is a prerequisite for the formation of the majority of large-scale flow units on Venus.

Magee, Kari P.↗
NASA NTRS1992

Deep infrared galaxies

High signal-to-noise ratio optical spectra of 17 infrared-bright emission-line galaxies near the north ecliptic pole are presented. Reddening-corrected line ratios forbidden O III 5007/H-beta, N II 6583/H-alpha, S II (6716 + 6731)/H-alpha, and O I 6300/H-alpha are used to discriminate between candidate energy generation mechanisms in each galaxy. These criteria have frequently been applied to optically selected samples of galaxies in the past, but this is the first time they have been applied to a set of faint flux-limited infrared-selected objects. The analysis indicates the sample contains seven starburst galaxies and three (AGN). However, seven galaxies in the present sample elude the classification scheme based on these line ratios. It is concluded that a two-component (starburst plus AGN) model for energy generation is inadequate for infrared galaxies.

Ashby, Matthew↗
NASA NTRS2002

Double Layers in Expanding Plasmas and Their Relevance to the Auroral Plasma Processes

When a dense plasma consisting of a cold and a sufficiently warm electron population expands, a rarefaction shock forms. In the expansion of the polar wind in the magnetosphere, it has been previously shown that when a sufficiently warm electron population also exists, in addition to the usual cold ionospheric one, a discontinuity forms in the electrostatic potential distribution along the magnetic field lines. Despite the lack of spatial resolution and the assumption of quasi-neutrality in the polar wind models, such discontinuities have been called double layers (DLs). Recently similar discontinuities have been invoked to partly explain the auroral acceleration of electrons and ions in the upward current region. By means of one-dimensional Vlasov simulations of expanding plasmas, for the first time we make here the connection between (i) the rarefaction shocks, (ii) the discontinuities in the potential distributions, and (iii) DLs. We show that when plasmas expand from opposite directions into a deep density cavity with a potential drop across it and when the plasma on the high-potential side contains two electron populations, the temporal evolution of the potential and the plasma. distribution generates evolving multiple double layers with an extended density cavity between them. One of the DLs is the rarefaction-shock (RFS) and it forms by the reflections of the cold electrons coming from the high-potential side; it supports a part of the potential drop approximately determined by the hot electron temperature.

Singh, Nagendra↗
NASA NTRS2011

Earth as an Extrasolar Planet: Earth Model Validation Using EPOXI Earth Observations

The EPOXI Discovery Mission of Opportunity reused the Deep Impact flyby spacecraft to obtain spatially and temporally resolved visible photometric and moderate resolution near-infrared (NIR) spectroscopic observations of Earth. These remote observations provide a rigorous validation of whole disk Earth model simulations used to better under- stand remotely detectable extrasolar planet characteristics. We have used these data to upgrade, correct, and validate the NASA Astrobiology Institute s Virtual Planetary Laboratory three-dimensional line-by-line, multiple-scattering spectral Earth model (Tinetti et al., 2006a,b). This comprehensive model now includes specular reflectance from the ocean and explicitly includes atmospheric effects such as Rayleigh scattering, gas absorption, and temperature structure. We have used this model to generate spatially and temporally resolved synthetic spectra and images of Earth for the dates of EPOXI observation. Model parameters were varied to yield an optimum fit to the data. We found that a minimum spatial resolution of approx.100 pixels on the visible disk, and four categories of water clouds, which were defined using observed cloud positions and optical thicknesses, were needed to yield acceptable fits. The validated model provides a simultaneous fit to the Earth s lightcurve, absolute brightness, and spectral data, with a root-mean-square error of typically less than 3% for the multiwavelength lightcurves, and residuals of approx.10% for the absolute brightness throughout the visible and NIR spectral range. We extend our validation into the mid-infrared by comparing the model to high spectral resolution observations of Earth from the Atmospheric Infrared Sounder, obtaining a fit with residuals of approx.7%, and temperature errors of less than 1K in the atmospheric window. For the purpose of understanding the observable characteristics of the distant Earth at arbitrary viewing geometry and observing cadence, our validated forward model can be used to simulate Earth s time dependent brightness and spectral properties for wavelengths from the far ultraviolet to the far infrared.brightness

Robinson, Tyler D.↗
DOE OSTI2025

yProv4ML: Effortless provenance tracking for machine learning systems

The rapid growth in interest in deep learning and foundation models (FMs) in particular, has attracted the attention of a diverse range of researchers thanks to their generalization ability. However, the advent of these techniques has also brought to light the lack of transparency and rigor in the way development is pursued. In particular, the inability to determine the number of epochs and other hyperparameters in advance presents challenges in identifying the best model. To address this challenge, machine learning frameworks such as MLFlow can automate the collection of this type of information. However, these tools capture data using proprietary formats and pose little attention to lineage. This paper proposes yProv4ML, a framework that captures provenance information generated during machine learning processes in PROV-JSON format, with minimal code modification.

Machine learning↗
DOE OSTI2024

MSD CoP Webinar: AI and Extreme Events - Overcoming Data Challenges for Improved Characterization of Climate Extremes

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: Artificial Intelligence (AI) models require large volumes of data for training and testing. Data requirements present challenges for using AI to explore extreme events with limited observational data. This webinar will showcase two innovative methods developed by part of the European Climate Intelligence (CLINT) project to overcome data challenges and harness AI to improve our understanding of climate extremes. Dr. Ascenso will present his research on data augmentation methods to improve estimates of tropical cyclones using satellite data. His presentation will review established methods for data augmentation and explore opportunities and challenges for using generative AI to generate images of extreme, life-threatening tropical cyclones. Next, Dr. Plesiat will present his research on deep learning techniques to overcome limited observational data sets. His presentation will illustrate deep learning methods to develop AI reconstructions of four climate indices across Europe. Presenters : Dr. Guido Ascenso (post-doctoral researcher, Politecnico di Milano); Dr. Étienne Plésiat (German Climate Computing Centre - DKRZ) Moderator(s): Stefano Galelli (MSD CoP WG Co-Lead), David Gold (MSD CoP WG Co-Lead), Jillian Sturtevant (MSD CoP WG Communications Officer), Matteo Giuliani (Politecnico di Milano, MSD CoP WG Member, Moderator and Organizer) This webinar was held on: October 11, 2024 from 11AM - 1PM ET

AI↗
NASA NTRS1995

Spectral and Temporal Characteristics of X-Ray-Bright Stars in the Pleiades

We follow up our deep ROSAT imaging survey of the Pleiades (Stauffer et al. 1994) with an analysis of the spectral and temporal characteristics of the X-ray-bright stars in the Pleiades. Raymond & Smith (1977) one and two-temperature models have been used to fit the position-sensitive proportional counter (PSPC) pulse-height spectra of the dozen or so brightest sources associated with late-type Pleiades members. The best-fit temperatures suggest hot coronal temperatures for K, M, and rapidly rotating G stars, and cooler temperatures for F and slowly rotating G stars. In order to probe the many less X-ray-luminous stars, we have generated composite spectra by combining net counts from all Pleiades members according to spectral type and rotational velocity. Model fits to the composite spectra confirm the trend seen in the individual spectral fits. Particularly interesting is the apparent dependence of coronal temperature on L(sub x)/L(sub bol). A hardness-ratio analysis also confirms some of these trends. The PSPC data have also revealed a dozen or so strong X-ray flares with peak X-ray luminosities in excess of approx. 10(exp 30) ergs/sec. We have modeled the brightest of these flares with a simple quasi-static cooling loop model. The peak temperature and emission measure and the inferred electron density and plasma volume suggest a very large scale flaring event. The PSPC data were collected over a period of approx. 18 months, allowing us to search for source variability on timescales ranging from less than a day (in the case of flares) to more than a year between individual exposures. On approximately year-long timescales, roughly 25% of the late-type stars are variable. Since the Pleiades was also intensively monitored by the imaging instruments on the Einstein Observatory, we have examined X-ray luminosity variations on the 10 yr timescale between Einstein and ROSAT and find that up to 40% of the late-type stars are X-ray variable. Since there is only marginal evidence for increased variability on decade-long timescales, the variability observed on long and short timescales may have a common physical origin.

Gagne, Marc↗
NASA NTRS2001

Next-Generation Ion Propulsion Being Developed

The NASA Glenn Research Center ion-propulsion program addresses the need for high specific-impulse systems and technology across a broad range of mission applications and power levels. One activity is the development of the next-generation ion-propulsion system as a follow-on to the successful Deep Space 1 system. The system is envisioned to incorporate a lightweight ion engine that can operate over 1 to 10 kW, with a 550-kg propellant throughput capacity. The engine concept under development has a 40-cm beam diameter, twice the effective area of the Deep Space 1 engine. It incorporates mechanical features and operating conditions to maximize the design heritage established by the Deep Space 1 engine, while incorporating new technology where warranted to extend the power and throughput capability. Prototype versions of the engine have been fabricated and are under test at NASA, with an engineering model version in manufacturing. Preliminary performance data for the prototype engine have been documented over 1.1- to 7.3-kW input power. At 7.3 kW, the engine efficiency is 0.68, at 3615-sec specific impulse. Critical component temperatures, including those of the discharge cathode assembly and magnets, have been documented and are within established limits, with significant margins relative to the Deep Space 1 engine. The 1- to 10-kW ion thruster approach described here was found to provide the needed power and performance improvement to enable important NASA missions. The Integrated In-Space Transportation Planning (IISTP) studies compared many potential technologies for various NASA, Government, and commercial missions. These studies indicated that a high-power ion propulsion system is the most important technology for development because of its outstanding performance versus perceived development and recurring costs for interplanetary solar electric propulsion missions. One of the best applications of a highpower electric propulsion system was as an integral part of a solar electric propulsion (SEP) stage to send a payload to outer planet targets. The IISTP studies showed that either trip time or launch vehicle class could be significantly reduced when compared with state-of-the-art systems.

Patterson, Michael J.↗
DOE OSTI2026

Key Drivers for Offshore Wind Deployment in the Western United States

The western coast of the United States has abundant natural resources, including wind, sun, and water. Despite strong ocean winds, offshore wind energy (OSW) in the Western United States is in the early stages of technology development due largely to the deep water off the coast, which requires floating platforms rather than fixed‐bottom turbines. Floating OSW increases the technical difficulty of installation and thus the cost relative to both fixed‐bottom OSW and land‐based turbines. OSW is a potential generation option to help meet increasing demand on the west coast of the United States because it likely has fewer land‐use conflicts than other technologies and complements sources in the existing electricity supply by providing energy during times of high system stress. This paper examines the possible drivers and barriers to OSW deployment on the West Coast using the National Laboratory of the Rockies' state‐of‐the‐art capacity expansion model and the Regional Energy Deployment System (ReEDS) model. We use ReEDS to explore a multitude of future scenarios looking at key drivers for OSW deployment, including variations on the cost of OSW, electricity demand growth, and the availability of competing technologies to examine the factors that may play a role in OSW growth. Assuming coastal state policies such as renewable portfolio and clean energy standards remain in place, we find that OSW can play a role in meeting electricity demand and provide energy during stressful grid conditions and find that deployment from the least‐cost investment model ranges from 7.6 to 38 GW by 2045.

17 WIND ENERGY↗
NASA NTRS1995

Performance of a Ka-band transponder breadboard for deep-space applications

This article summarizes the design concepts applied in the development of and advanced Ka-band (34.4 GHz/32 GHz) transponder breadboard for the next generation of space communications systems applications. The selected architecture upgrades the X-band (7.2 GHz/8.4 GHz) deep-space transponder (DST) to provide Da-band up/Ka- and X-band down capability. The Ka-band transponder breadboard incorporates several state-of-the-art components, including sampling mixers, a Ka-band dielectric resonator oscillator, and microwave monolithic integrated circuits (MMICs). The MMICs that were tested in the breadboard include upconverters, downconverters, automatic gain control circuits, mixers, phase modulators, and amplifiers. The measured receiver dynamic range, tracking range, acquisition rate, static phase error, and phase jitter characteristics of the Ka-band breadboard interfaced to the advanced engineering model X-band DST are in good agreement with the expected performance. The results show a receiver tracking threshold of -149 dBm with a dynamic range of 80 dB and a downlink phase jitter of 7 deg rms. The analytical results of phase noise and Allan standard deviation are in good agreement with the experimental results.

Mysoor, N. R.↗
DOE OSTI2024

The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning

Ab initio molecular dynamics (AIMD) simulations have become an important tool used in the construction of equations of state (EOS) tables for warm dense matter. Due to computational costs, only a limited number of system state conditions can be simulated, and the remaining EOS surface must be interpolated for use in radiation-hydrodynamic simulations of experiments. In this work, we develop a thermodynamically consistent EOS model that utilizes a physics-informed machine learning approach to implicitly learn the underlying Helmholtz free-energy from AIMD generated energies and pressures. The model, referred to as PIML-EOS, was trained and tested on warm dense polystyrene producing a fit within a 1% relative error for both energy and pressure and is shown to satisfy both the Maxwell and Gibbs–Duhem relations. In addition, we provide a path toward obtaining thermodynamic quantities, such as the total entropy and chemical potential (containing both ionic and electronic contributions), which are not available from current AIMD simulations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Generating An Advanced Cross-section Library For HTGR Pebble Bed Depletion Calculations Using Reduced-Order Model Generation Techniques

For code development, Advanced Reactor Technologies - Gas Cooled Reactors Program (ART-GCR) rely on a collaboration with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, but the cross sections generation and the methodology definition is part of this program area goals. Based on previous studies in FY23, the size of microscopic cross section libraries increases rapidly with the number of tabulations, requiring significant amount of memory and drastically slowing down the Griffin calculations when evaluating cross sections via the multivariate linear interpolation approach. Rising to these challenges, this work investigates constructing Reduced-order Models (ROMs) for the multi-group microscopic cross sections to accelerate the cross section evaluation in Griffin. A database of multigroup cross sections is first collected considering all possible parameters that a designer could change for optimization. Down-selection of the ROM techniques afterward shows Deep Neural Network (DNN) as the best candidate when jointly consider memory efficiency, predictive accuracy, computational cost, scalability, flexibility and ease of implementation of the algorithms in comparison to the multidimensional interpolation. This work develops a specific interface that enables the cross section predictions using pre-trained DNN models into Griffin leveraging the existing ROM capabilities. DNNs have been trained for all isotopes for use in Griffin. Preliminary Griffin testing shows that DNNs exhibit exceptional predictive accuracy and the use of DNNs provides orders of magnitude improvement in memory efficiency compared to conventional interpolation techniques. With such ROM techniques, it holds great promise to further increase the fidelity of the Pebble Bed Reactor (PBR) simulation by increasing the number of tabulations/state variables during cross section evaluation, while maintaining the computational cost affordable in Griffin.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2006

Update On the Status of the FLUKA Monte Carlo Transport Code*

The FLUKA Monte Carlo transport code is a well-known simulation tool in High Energy Physics. FLUKA is a dynamic tool in the sense that it is being continually updated and improved by the authors. We review the progress achieved since the last CHEP Conference on the physics models, some technical improvements to the code and some recent applications. From the point of view of the physics, improvements have been made with the extension of PEANUT to higher energies for p, n, pi, pbar/nbar and for nbars down to the lowest energies, the addition of the online capability to evolve radioactive products and get subsequent dose rates, upgrading of the treatment of EM interactions with the elimination of the need to separately prepare preprocessed files. A new coherent photon scattering model, an updated treatment of the photo-electric effect, an improved pair production model, new photon cross sections from the LLNL Cullen database have been implemented. In the field of nucleus-- nucleus interactions the electromagnetic dissociation of heavy ions has been added along with the extension of the interaction models for some nuclide pairs to energies below 100 MeV/A using the BME approach, as well as the development of an improved QMD model for intermediate energies. Both DPMJET 2.53 and 3 remain available along with rQMD 2.4 for heavy ion interactions above 100 MeV/A. Technical improvements include the ability to use parentheses in setting up the combinatorial geometry, the introduction of pre-processor directives in the input stream. a new random number generator with full 64 bit randomness, new routines for mathematical special functions (adapted from SLATEC). Finally, work is progressing on the deployment of a user-friendly GUI input interface as well as a CAD-like geometry creation and visualization tool. On the application front, FLUKA has been used to extensively evaluate the potential space radiation effects on astronauts for future deep space missions, the activation dose for beam target areas, dose calculations for radiation therapy as well as being adapted for use in the simulation of events in the ALICE detector at the LHC.

Ferrari, A.↗
NASA NTRSDate not supplied

Ground-testing of One-way Ranging from a Lunar Beacon Demonstrator Payload (Lunar Node -1)

Similar to lighthouses providing navigation aids and awareness to ships sailing along the coast, in-situ navigation beacons can be used to provide redundancy, awareness, and improved onboard position knowledge for vehicles operating in and around the lunar sphere of influence. Lunar Node - 1 is a radio frequency navigation beacon that is manifested on Intuitive Machines' NOVA-C lunar lander flight in 2022 for operation during cruise and from a mid-latitude landing location. This payload utilizes commercial components developed for Low Earth Orbit applications and packages them into an integrated package to provide a navigation reference signal as part of a large lunar-centric navigation network. As part of its operations, Deep Space Network ground stations will operate as the notional "user" of this service, allowing for performance characterization, calibration, and operation of the beacon payload. This paper provides an overview of the payload, how its fits into a large concept of operations, and ground testing results. A specific focus is on ground testing of the payload with DSN ground receivers as part of RF Compatibility Testing. A primary goal of this testing was to characterize a pseudo-noise ranging code sequence generated by the payload. The results in this paper define the testing sequence, unique operational constraints with two-way ranging ground equipment, and applications of the ground-derived error model to predicted in-flight performance of the ranging code.

Evan Anzalone↗
NASA NTRS1972

Electrical conductivity of condensed molecular hydrogen in the giant planets

Theoretical interpretation of several phenomena concerning Jupiter and Saturn depends upon the electrical conductivity of molecular hydrogen which, according to present models, forms the outermost layer of both planets. The layer starts at the transition pressure between the metallic and the molecular form of hydrogen, that is around 1 Mbar, and extends to the outside limits of the atmosphere. Whether at the highest pressures (and temperatures) this layer is a solid or a dense fluid is not certain. In any case, the fluid is in supercritical condition so that there is only a gradual transition from dense liquid to a gaseous form. The two theories which require specific values of the conductivity of the condensed molecular hydrogen are those pertaining to the generation of a magnetic field in the liquid hydrogen rather than in the deep metallic interior (HIDE, 1967), and those concerned with the electromagnetic coupling and exchange of angular momentum between the liquid core and the solid molecular hydrogen mantle.

Smoluchowski, R.↗
DOE OSTI2024

Semantic Stealth: Crafting Covert Adversarial Patches for Sentiment Classifiers Using Large Language Models

Deep learning models have been shown to be vulnerable to adversarial attacks, in which perturbations to their inputs cause the model to produce incorrect predictions. As opposed to adversarial attacks in computer vision, where small changes introduced to pixel values can drastically alter a model's output while remaining imperceptible to humans, text-based attacks are difficult to conceal due to the discrete nature of tokens. Consequently, unconstrained gradient-based attacks often produce adversarial examples that lack semantic meaning, rendering them detectable through visual inspection or perplexity filters. In contrast to methods that rely on gradient-based optimization in the embedding space, we propose an approach that leverages a Large Language Model's ability to generate grammatically correct and semantically meaningful text to craft adversarial patches that seamlessly blend in with the original input text. These patches can be used to alter the behavior of a target model, such as a text classifier. Since our approach does not rely on gradient backpropagation, it only requires access to the target model's confidence scores, making it a grey-box attack. We demonstrate the feasibility of our approach using open-source LLMs, including Intel's Neural Chat, Llama2, and Mistral-Instruct, to generate adversarial patches capable of altering the predictions of a distilBERT model fine-tuned on the IMDB reviews dataset for sentiment classification.

Roa Carvajal, Maria↗
DOE OSTI2025

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

FAIR Surrogate Benchmarks Supporting AI and Simulation Research (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia (UVA). SBI repositories include data, code, and all relevant collateral artifacts that the science and engineering community need to use and reuse these data sets and surrogates. SBI repositories generate active research from both the participants in SBI and the broad community of AI and domain scientists. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and captures them as surrogate benchmarks with a rich set of metadata covering: Data; Model; Metrics specification; Machine specification; and Science, Speed, and Power Results. We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non-Surrogate benchmarks that have many common features and similar issues as regards FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, Benchmarks have datasets, models, and metadata and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗