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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Modeling Global Vegetation Gross Primary Productivity, Transpiration and Hyperspectral Canopy Radiative Transfer Simultaneously Using a Next Generation Land Surface Model—CliMA Land

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400–2,500 nm for reflectance, 640–850 nm for fluorescence) at hourly time step and 1° spatial resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun-sensor geometry. The spatial patterns of modeled GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data-driven products (mean R 2 = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, for example, providing insights into when GPP, SIF, and NIRv diverge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images

The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All the code and models are availabe at: https://github.com/mvrl/SimLBR

Dhakal, Aayush [Washington University, St. Louis]↗
DOE OSTI2023

Machine learning simulation of finite element analysis in augmented reality

Media, method and system for approximating a finite element analysis texture map for an object. To accomplish this, the object is converted to a computer generated model and finite element analysis is performed for a plurality of different simulated inputs to generate a plurality of simulated mappings. Each simulated mapping is converted into a simulated texture map. A machine learning model is trained on the simulated inputs and simulated texture maps to generate a texture map which approximates a finite element analysis. The machine learning model receives a user input and generates the texture map therefrom. The texture map is then wrapped to the object and displayed.

Scherer, Derek Carl↗
DOE OSTI2024

DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense , a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble space telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model’s training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.

Computer Science↗
DOE OSTI2026

AI-Driven Frameworks for Characterizing Urban Energy Systems

We develop AI-driven frameworks to characterize urban energy systems with the goal of transforming planning by reducing the labor of model generation, scaling scenario exploration, and improving accuracy for localized analysis. The approach integrates top-down and bottom-up data to train different AI models that predict missing information and generate inputs and targeted scenarios for district-scale models. The result is a scalable framework that provides actionable insights for reliable and efficient planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

SDSS-IV MaStar: Stellar parameter determination with continuum-supplemented full-spectrum fitting

We present a stellar parameter catalog built to accompany the MaStar Stellar Library, which is a comprehensive collection of empirical, medium-resolution stellar spectra. We constructed this parameter catalog by using a multicomponent χ 2 fitting approach to match MaStar spectra to models generated by interpolating the ATLAS9-based BOSZ model spectra. The total χ 2 for a given model is defined as the sum of components constructed to characterize narrow-band features of observed spectra (e.g., absorption lines) and the broadband continuum shape separately. Extinction and systematics due to flux calibration were taken into account in the fitting. The χ 2 distribution for a given region of model space was sampled using a Markov chain Monte Carlo (MCMC) algorithm, the data from which were then used to extract atmospheric parameter estimates (T eff , log g, [Fe/H], and [α/Fe]), their corresponding uncertainties, and direct extinction measurements. Two methods were used to extract parameters and uncertainties: one that accepts the MCMC’s prescribed minimum-χ 2 result, and one that uses Bayesian inference to compute a likelihood-weighted mean from the χ 2 distribution sampled by the MCMC. Results were evaluated for internal consistency using repeat observations where available and by comparing them with external data sets (e.g., APOGEE-2 and Gaia DR2). Our spectral-fitting exercise reveals possible deficiencies in current theoretical model spectra, illustrating the potential power of MaStar spectra for helping to improve the models. This paper represents an update to the parameters that were originally presented with SDSS-IV DR17. The MaStar parameter catalog containing our BestFit results is available on the SDSS-IV DR17 website as part of version 2 of the MaStar stellar parameter value-added catalog.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The 1.3 Å resolution structure of the truncated group Ia type IV pilin from Pseudomonas aeruginosa strain P1

The type IV pilus is a diverse molecular machine capable of conferring a variety of functions and is produced by a wide range of bacterial species. The ability of the pilus to perform host-cell adherence makes it a viable target for the development of vaccines against infection by human pathogens such as Pseudomonas aeruginosa . Here, the 1.3 Å resolution crystal structure of the N-terminally truncated type IV pilin from P. aeruginosa strain P1 (ΔP1) is reported, the first structure of its phylogenetically linked group (group I) to be discussed in the literature. The structure was solved from X-ray diffraction data that were collected 20 years ago with a molecular-replacement search model generated using AlphaFold ; the effectiveness of other search models was analyzed. Examination of the high-resolution ΔP1 structure revealed a solvent network that aids in maintaining the fold of the protein. On comparing the sequence and structure of P1 with a variety of type IV pilins, it was observed that there are cases of higher structural similarities between the phylogenetic groups of P. aeruginosa than there are between the same phylogenetic group, indicating that a structural grouping of pilins may be necessary in developing antivirulence drugs and vaccines. These analyses also identified the α–β loop as the most structurally diverse domain of the pilins, which could allow it to serve a role in pilus recognition. Studies of ΔP1 in vitro polymerization demonstrate that the optimal hydrophobic catalyst for the oligomerization of the pilus from strain K122 is not conducive for pilus formation of ΔP1; a model of a three-start helical assembly using the ΔP1 structure indicates that the α–β loop and the D-loop prevent in vitro polymerization.

Bragagnolo, Nicholas↗
DOE OSTI2021

Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification

Full-waveform inversion (FWI) is an accurate imaging approach for modeling the velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, the strong nonlinearity of FWI resulting from fitting oscillatory waveforms can trap the optimization in local minima. We have adopted a neural-network-based full-waveform inversion (NNFWI) method that integrates deep neural networks with FWI by representing the velocity model with a generative neural network. Neural networks can naturally introduce spatial correlations as regularization to the generated velocity model, which suppresses noise in the gradients and mitigates local minima. Furthermore, the velocity model generated by neural networks is input to the same partial differential equation (PDE) solvers used in conventional FWI. The gradients of the neural networks and PDEs are calculated using automatic differentiation, which back propagates gradients through the acoustic PDEs and neural network layers to update the weights of the generative neural network. Experiments on 1D velocity models, the Marmousi model, and the 2004 BP model determine that NNFWI can mitigate local minima, especially for imaging high-contrast features such as salt bodies, and it significantly improves the inversion in the presence of noise. Adding dropout layers to the neural network model also allows analyzing the uncertainty of the inversion results through Monte Carlo dropout. NNFWI opens a new pathway to combine deep learning and FWI for exploiting the characteristics of deep neural networks and the high accuracy of PDE solvers. Because NNFWI does not require extra training data and optimization loops, it provides an attractive and straightforward alternative to conventional FWI.

58 GEOSCIENCES↗
DOE OSTI2020

Variability and Diversity Load Model Tool [SWR-20-03]

The motivation for the development of this tool and the underlying algorithms and methods was to enable the development of high-temporal resolution, realistic time-series data for quasi-static time-series (QSTS) analysis of distribution systems. Often, aggregated load profile data for a distribution circuit is available (e.g. feeder loading data collected via SCADA at the utility substation) and, while this data is typically accurate it masks the considerable variability of the 100’s or 1000’s of individual loads connected on the circuit. This tool was developed to model both the increased variability expected for these individual loads (e.g. the load of a single distribution transformer connected to 8-12 houses) and the expected diversity between loads on the circuit. It is important to note that the difference in variability and diversity, in the context of this tool, is that variability modeling only adds representative variability due to disaggregated load characteristics (e.g. the presence in the load profile of loads turning off and on like an air conditioner/oven) while the average energy profile remains the same as the user supplied power profile. Diversity modeling generates multiple individual load profiles which, in aggregate, sum to the user supplied power profile. Diversity is effectively variability in the energy usage over longer periods of time than seen in the variability model. Put another way, variability modeling supplies the expected variability due to the operation of various end-use loads and diversity modeling supplies the usage differences due to human behavior, schedules, etc. This load modeling tool was developed for use in generating data for distribution systems. Modeling is summarized by two major functions: 1) taking low resolution load profiles and adding intra-seconds variability onto the profiles, and 2) taking a user supplied load profile and distribution factors and adding both diversity and variability to the user supplied profile.

Zhu, Xiangqi↗
DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗
DOE OSTI2021

Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions

Abstract The potential advantage of machine learning in quantum computers is a topic of intense discussion in the literature. Theoretical, numerical, and experimental explorations will most likely be required to understand its power. There have been different algorithms proposed to exploit the probabilistic nature of variational quantum circuits for generative modeling. In this paper, a hybrid architecture for quantum generative adversarial networks (QGANs) is employed and their robustness in the presence of noise is studied. A simple way of adding different types of noise to the quantum generator circuit is devised, and the noisy hybrid QGANs (HQGANs) are simulated numerically to learn continuous probability distributions, and to show that the performance of HQGANs remains unaffected. The effect of different parameters on the training time is also investigated to reduce the computational scaling of the algorithm and simplify its deployment on a quantum computer. The training on Rigetti's Aspen‐4‐2Q‐A quantum processing unit is then performed, and the results from the training are presented. The authors' results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate‐scale quantum devices.

Anand, Abhinav↗
DOE OSTI2024

Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Dynamic consensus and extended high gain observers as a tool to achieve practical frequency synchronization in power systems under unknown time-varying power demand

In this paper, we consider the frequency synchronization problem in a network of lossless, connected, and network-reduced power system. Frequency synchronization is an important problem in power systems as frequency deviation can lead to degraded power quality, tripping of generators, etc. We present a load-estimator-based consensus algorithm in a network of fourth-order generator models that achieves practical frequency synchronization in the presence of unknown time-varying power demand. We do not assume that the demand is generated by a known model. We follow a multiple time-scale approach for the stability analysis. It is shown that synchronization is achieved for the case of constant power demand, by showing that the system is exponentially stable. Furthermore, for the case of time-varying power demand, we show the error trajectories are ultimately bounded, and the synchronization error is proportional to an upper bound on the derivative of the time-varying power demand. We simulate the controller performance on a four area network which is equivalent to the IEEE New England 39-Bus system.

42 ENGINEERING↗
DOE OSTI2021

Association‐based activity coefficient model for electrolyte solutions

Abstract This work presents an association‐based activity coefficient model that explicitly considers the solution nonideality due to associations among ions and solvent species. Built upon the electrolyte nonrandom two‐liquid (eNRTL) model, the model greatly improves the accuracy of eNRTL model for strongly associating electrolyte solutions due to presence of ionic species with high surface charge density. The model successfully correlates mean ionic activity coefficients of 46 aqueous single‐salt systems from 10 cations and 5 anions at 298.15 K up to their solubility limits. With the ion‐specific association parameters identified, the model accurately predicts activity and osmotic coefficients for aqueous mixed‐salt systems at 298.15 K. The temperature dependence of the model results has also been examined at 273–373 K. With superior accuracy over a wide range of concentration and temperature, the model represents a major advancement over eNRTL model and has a great potential to be a next‐generation model for electrolyte solutions.

Lin, Yu‐Jeng↗
DOE OSTI2023

Assembly of JAZ–JAZ and JAZ–NINJA complexes in jasmonate signaling

Jasmonates (JAs) are plant hormones with crucial roles in development and stress resilience. They activate MYC transcription factors by mediating the proteolysis of MYC inhibitors called JAZ proteins. In the absence of JA, JAZ proteins bind and inhibit MYC through the assembly of MYC–JAZ–Novel Interactor of JAZ (NINJA)–TPL repressor complexes. However, JAZ and NINJA are predicted to be largely intrinsically unstructured, which has precluded their experimental structure determination. Through a combination of biochemical, mutational, and biophysical analyses and AlphaFold-derived ColabFold modeling, we characterized JAZ–JAZ and JAZ–NINJA interactions and generated models with detailed, high-confidence domain interfaces. We demonstrate that JAZ, NINJA, and MYC interface domains are dynamic in isolation and become stabilized in a stepwise order upon complex assembly. By contrast, most JAZ and NINJA regions outside of the interfaces remain highly dynamic and cannot be modeled in a single conformation. Our data indicate that the small JAZ Zinc finger expressed in Inflorescence Meristem (ZIM) motif mediates JAZ–JAZ and JAZ–NINJA interactions through separate surfaces, and our data further suggest that NINJA modulates JAZ dimerization. This study advances our understanding of JA signaling by providing insights into the dynamics, interactions, and structure of the JAZ–NINJA core of the JA repressor complex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

AI-Improved Resolution Projections of Population Characteristics and Imperviousness Can Improve Resolution and Accuracy of Urban Flood Predictions

Focal Area: (2) Predictive modeling through the use of AI techniques and AI-derived model components. Specifically, we call for the use of deep generative models from ML for providing high-resolution projections of impervious surface area, as well as neural network solvers for fast approximation of urban hydrodynamics to provide greatly enhanced forecasts of future urban flood dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A guide on modeling electrostatics of semiconductor detectors in COMSOL Multiphysics ® for DRiFT

Adding a new semiconductor detector into the detector response function toolkit (DRiFT) requires a model of the electric potential and the electric field. To get an accurate electrostatics model, the geometry of the detector should be modeled as closely to the real geometry as feasible, including the semiconductor materials, doping layers and concentrations, and contacts. Since the electric field and potential are used to calculate the induced signal, the results of the detector response functions are largely influenced by the electrostatics models. Initial electrostatics models were completed using Silvaco, however, this document provides guidance on using COMSOL Mulitphysics ® (COMSOL) to model the detectors to give users additional flexibility. Guidance on using COMSOL and its user interface are mostly left out of this document, but best practices for geometry, modeling methods, and data format/exporting are included to help users generate models that are compatible with DRiFT, prevents bugs, issues, and inaccuracies during charge collection calculations. The following summary provides an overview of the document.

42 ENGINEERING↗
DOE OSTI2023

Fast and accurate simulations of calorimeter showers with normalizing flows

In this study, we introduce caloflow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce many-channel calorimeter showers with extremely high fidelity, providing a fresh alternative to computationally expensive geant4 simulations, as well as other state-of-the-art fast simulation frameworks based on generative adversarial networks (GANs) or variational autoencoders (VAEs). In addition to the usual histograms of physical features and images of calorimeter showers, we introduce a new metric for judging the quality of generative modeling: the performance of a classifier trained to differentiate real from generated images. We show that GAN-generated images can be identified by the classifier with nearly 100% accuracy, while images generated from caloflow are better able to fool the classifier. More broadly, normalizing flows offer several advantages compared to other state-of-the-art approaches (GANs and VAEs), including tractable likelihoods, stable and convergent training, and principled model selection. Normalizing flows also provide a bijective mapping between data and the latent space, which could have other applications beyond simulation, for example, to detector unfolding.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗