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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
DOE OSTI2015

UNSUPERVISED TRANSIENT LIGHT CURVE ANALYSIS VIA HIERARCHICAL BAYESIAN INFERENCE

Historically, light curve studies of supernovae (SNe) and other transient classes have focused on individual objects with copious and high signal-to-noise observations. In the nascent era of wide field transient searches, objects with detailed observations are decreasing as a fraction of the overall known SN population, and this strategy sacrifices the majority of the information contained in the data about the underlying population of transients. A population level modeling approach, simultaneously fitting all available observations of objects in a transient sub-class of interest, fully mines the data to infer the properties of the population and avoids certain systematic biases. We present a novel hierarchical Bayesian statistical model for population level modeling of transient light curves, and discuss its implementation using an efficient Hamiltonian Monte Carlo technique. As a test case, we apply this model to the Type IIP SN sample from the Pan-STARRS1 Medium Deep Survey, consisting of 18,837 photometric observations of 76 SNe, corresponding to a joint posterior distribution with 9176 parameters under our model. Our hierarchical model fits provide improved constraints on light curve parameters relevant to the physical properties of their progenitor stars relative to modeling individual light curves alone. Moreover, we directly evaluate the probability for occurrence rates of unseen light curve characteristics from the model hyperparameters, addressing observational biases in survey methodology. We view this modeling framework as an unsupervised machine learning technique with the ability to maximize scientific returns from data to be collected by future wide field transient searches like LSST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Hybrid geological modeling: Combining machine learning and multiple-point statistics

Accurately modeling and constructing a geologically realistic subsurface model remains an outstanding problem as the morphology controls the flow behaviors. Particularly, one of the pattern-based methods, namely cross-correlation based simulation, has been proved to be an effective way to reconstruct a realistic model, at both small and large scales. However, conditioning to point data in the large-scale problems is still a crucial issue in these algorithms, since there is always a trade-off between the quality of the realizations and the degree of point data reproduction. Specifically, it is not practical to build a training image (TI) which includes all the possibilities and variabilities. Therefore, finding a pattern that can represent the point data and, at the same time, preserving the connectivities is difficult. This leads to producing highly-connected realizations with a significant mismatch or poor models with a reasonable degree of point data reproduction. To accurately reproduce the densely distributed hard data, pixel-based methods can also produce some unrealistic artifacts around the hard data. In this paper, to overcome this challenge, however, we use pattern-based methods as they often produce more disconnected geobodies when dealing with dense hard data, and proposed a hybrid algorithm using the pattern-based methods and convolutional neural network (CNN). The trained CNN model is utilized to improve the quality of conditioning to point data for the original realizations generated by the pattern-based algorithm. As such, the mismatch locations are identified, and the same regions are used in the training of CNN to mimic the procedure through which a missing region can be filled. To evaluate the performance of the proposed hybrid algorithm, it is tested on cases with different dimensions and different numbers of facies. Then, the newly improved realizations are compared with the initial realizations generated by the pattern-based algorithm. The comparison is also conducted by the flow simulation test. And it indicates that the proposed hybrid algorithm can better reproduce the point data, while the connectivities are better preserved.

58 GEOSCIENCES↗
DOE OSTI2015

Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective

Radiation oncology has always been deeply rooted in modeling, from the early days of isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) initiative. In recent years, medical modeling for both prognostic and therapeutic purposes has exploded thanks to increasing availability of electronic data and genomics. One promising direction that medical modeling is moving toward is adopting the same machine learning methods used by companies such as Google and Facebook to combat disease. Broadly defined, machine learning is a branch of computer science that deals with making predictions from complex data through statistical models. These methods serve to uncover patterns in data and are actively used in areas such as speech recognition, handwriting recognition, face recognition, “spam” filtering (junk email), and targeted advertising. Although multiple radiation oncology research groups have shown the value of applied machine learning (ML), clinical adoption has been slow due to the high barrier to understanding these complex models by clinicians. Here, we present a review of the use of ML to predict radiation therapy outcomes from the clinician's point of view with the hope that it lowers the “barrier to entry” for those without formal training in ML. We begin by describing 7 principles that one should consider when evaluating (or creating) an ML model in radiation oncology. We next introduce 3 popular ML methods—logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN)—and critique 3 seminal papers in the context of these principles. Although current studies are in exploratory stages, the overall methodology has progressively matured, and the field is ready for larger-scale further investigation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Electron percolation in realistic models of carbon nanotube networks

The influence of penetrable and curved carbon nanotubes (CNT) on the charge percolation in three-dimensional disordered CNT networks have been studied with Monte-Carlo simulations. By considering carbon nanotubes as solid objects but where the overlap between their electron cloud can be controlled, we observed that the structural characteristics of networks containing lower aspect ratio CNT are highly sensitive to the degree of penetration between crossed nanotubes. Following our efficient strategy to displace CNT to different positions to create more realistic statistical models, we conclude that the connectivity between objects increases with the hard-core/soft-shell radii ratio. In contrast, the presence of curved CNT in the random networks leads to an increasing percolation threshold and to a decreasing electrical conductivity at saturation. The waviness of CNT decreases the effective distance between the nanotube extremities, hence reducing their connectivity and degrading their electrical properties. We present the results of our simulation in terms of thickness of the CNT network from which simple structural parameters such as the volume fraction or the carbon nanotube density can be accurately evaluated with our more realistic models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Evaluating Carbon Extremes in a Coupled Climate-Carbon Cycle Simulation

Gross primary production (GPP) measures the photosynthetic update of carbon by terrestrial ecosystems. Accurately quantifying and simulating GPP and its extremes remains a challenge in global carbon cycle sciences. Here, we evaluate GPP extremes in a coupled biogeochemistry (BGC) simulation by the Department of Energy's Energy Exascale Earth System Model (E3SMv1.1) using the Generalized Extreme Value (GEV) distribution statistical model. The simulation is evaluated against the Global Bio-Atmosphere Flux (GBAF) data. Temporal trends and ENSO dependence are also investigated by using GEV models where time and the Niño3.4 index are introduced as linear covariates. The E3SMv1.1 model simulation generally predicts stronger negative and positive GPP extremes as compared to GBAF data. It also tends to simulate stronger temporal trends of GPP extremes than GBAF data. While negative GPP extreme trends are not significant in either E3SM or GBAF, positive GPP trends are statistically significant over several regions only for the E3SMv1.1 model simulation. ENSO dependence is generally stronger in the E3SMv1.1 model simulation, but ENSO dependence is found not to be significant for the time period analyzed (1980-2006) to match GBAF data. For the longer simulation period of 1900-2006, ENSO dependence is found to be statistically significant over Amazon, the maritime continent and Northern Australia for both negative and positive extremes.

Xu, Min↗
DOE OSTI2015

Population of isomeric states in fusion and transfer reactions in beams of loosely bound nuclei near the Coulomb barrier

The influence of the mechanisms of nuclear reactions on the population of {sup 195m}Hg and {sup 197m}Hg(7/2{sup −}), {sup 198m}Tl and {sup 196m}Tl(7{sup +}), and {sup 196m}Au and {sub 198m}Au(12{sup −}) isomeric nuclear states obtained in reactions induced by beams of {sup 3}He, {sup 6}Li, and {sup 6}He weakly bound nuclei is studied. The behavior of excitation functions and high values of isomeric ratios (δ{sub m}/δ{sub g}) for products of nuclear reactions proceeding through a compound nucleus and involving neutron evaporation are explained within statistical models. Reactions in which the emission of charged particles occurs have various isomeric ratios depending on the reaction type. The isomeric ratio is lower in direct transfer reactions involving charged-particle emission than in reactions where the evaporation of charged particles occurs. Reactions accompanied by neutron transfer usually have a lower isomeric ratio, which behaves differently for different direct-reaction types (stripping versus pickup reactions)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

Gaikwad, Aniruddha (ORCID:0000000285642621)↗
DOE OSTI2024

Deep learning-based predictive models for laser direct drive at the Omega Laser Facility

The rich and complex physics of inertial confinement fusion provides a unique and challenging space for high-fidelity first-principles modeling. Consequently, simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this article, we present two deep-learning-based predictive models intended to address these difficulties. The first model (TL DNN) acts as a fast emulator of simulations as well as experiments at the Omega Laser Facility. This model is trained on a simulation database and subsequently calibrated on experimental data using transfer learning. To facilitate the development of this model, an autoencoder is developed to reduce the dimensionality of the input space by compressing the laser pulse input. The model predicts key experimental scalar observables of Omega experiments with high accuracy and minimal computational cost. This deep neural net enables rapid exploration of a high-dimensional input parameter space for an optimal implosion design. The second model (DNN SM+) aims to extend the statistical modeling work of Lees et al. [Phys. Rev. Lett. 127, 105001 (2021)], by increasing the complexity of the model space and allowing for coupling between degradation terms. Since the model capacity of DNN SM+ is higher than the model of Lees et al., DNN SM+ can potentially provide an improvement in predictive capability, and we use this model to provide insight into complicated degradation dependencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

InClass nets: independent classifier networks for nonparametric estimation of conditional independence mixture models and unsupervised classification

Abstract Conditional independence mixture models (CIMMs) are an important class of statistical models used in many fields of science. We introduce a novel unsupervised machine learning technique called the independent classifier networks (InClass nets) technique for the nonparameteric estimation of CIMMs. InClass nets consist of multiple independent classifier neural networks (NNs), which are trained simultaneously using suitable cost functions. Leveraging the ability of NNs to handle high-dimensional data, the conditionally independent variates of the model are allowed to be individually high-dimensional, which is the main advantage of the proposed technique over existing non-machine-learning-based approaches. Two new theorems on the nonparametric identifiability of bivariate CIMMs are derived in the form of a necessary and a (different) sufficient condition for a bivariate CIMM to be identifiable. We use the InClass nets technique to perform CIMM estimation successfully for several examples. We provide a public implementation as a Python package called RainDancesVI.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Validation of MCNP Critical Benchmark Models of PU-MET-FAST-016 [Slides]

The validation of PU-MET-FAST-016 contributed to the centralized LANL benchmark repository currently under development. The revisions made the MCNP models statistically, significantly more similar to the benchmark models. The overall impact on the USL is negligible. The revisions to the PU-MET-FAST-016 models provide value to the Los Alamos Benchmark Suite without invalidating past Whisper results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Predictive Indicators of the Performance of Large Language Models

In several mission contexts, it is desirable to estimate the performance of large language models (LLMs) on tasks that we cannot run directly. In light of published “scaling laws” our hypothesis is that some tasks should be consistently more challenging than others based on characteristics of the task. The goal of this project was to begin quantifying how much information about LLM performance can be gained from the features of a model and a task. Two of our statistical models struggled to converge. Pass/fail test results may provide limited information for inference beyond model quality and task difficulty, but we see no evidence at this time for significant feature interaction effect sizes, arguing for simple models. Future work extending the models to capitalize on perplexity of ground truth answers is suggested. This project also introduces “Depth of Knowledge Variant Testing” as a strategy for more finely assessing language models on open domain question and answer tasks. We developed sets of questions that ask a language model to produce similar information while demonstrating increasing depth of knowledge, and also relabeled existing Q&A test questions with their depth of knowledge. Our results suggest further consideration of Bloom’s taxonomy and further refinement of prompts to properly elicit information at varying depths. In the course of this work, we set up a basic infrastructure for standardizing tasks and testing many language models on these tasks. In addition to testing the predictive quality of model features and performance across test suites, with this project we have introduced two new task features to contextualize each test question: the Dewey Classification main category of information covered, and the Bloom’s taxonomy level that corresponds to the depth of knowledge probed by the question. Splits across these and other features produced over five hundred task subtypes with distinct feature vectors, which we tested on half a dozen models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Bayesian chain graph models to characterize microbe-environment dynamics

Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is problematic given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence, thus agreeing with the experimenter's intuition on the average behavior of nodes under treatment. The solution to our model is sparse via the Bayesian linear regression (LASSO). In addition, we propose an adaptive extension so that different shrinkages can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting, and compositional responses via an appropriate hierarchical structure. We test the performance of our model in a variety of simulated datasets, thereby showing superior performance to state-of-the-art approaches. We further apply our model to human gut and soil microbial compositional datasets, and we highlight that CG-LASSO can estimate biologically meaningful network structures in the data.

compositional data↗
NASA NTRS1973

Coordination procedure for radio relay and communication satellite services

A global rain rate statistic model is used to link microwave propagation statistics to measurable rain statistics in order to develop international telecommunication site criteria for radio relay and communication satellite services that minimize interference between receivers and transmitters. This rain coordination procedure utilizes a rain storm cell size, a statistical description of the rainfall rate within the cell valid for most of the earth's surface, approximations between Raleigh scatter and constancy of precipitation with altitude, and an analytic relation between radar reflectivity and rain rate.

Eckerman, J.↗
NASA NTRS1985

Magnetospheric plasma modeling (0-100 keV)

Spacecraft surface charging, which is primarily a current balance phenomenon, is in general a function of the dominant currents to and from the vehicle's surface. Within the near-earth magnetosphere the dominant currents to the surface are the ambient space plasma fluxes between approximately 0 and 100 keV. A major effort to understand the near-earth environment was initiated when spacecraft charging became a major issue. The present paper has the objective to summarize the basic features of the models which have resulted from this effort. A description is given of four categories of models, based primarily on the degree of empirical and theoretical input. Types of quantitative models are discussed, taking into account definitions, statistical models, analytic models, static models, and time-dependent models. Engineering models are also considered, giving attention to baseline models and 'worst-case' models.

Garrett, H. B.↗
NASA NTRS1992

Summertime photochemistry of the troposphere at high northern latitudes

Budgets of O3, NO(x), and NO(y), and acetic acid in the Arctic Boundary Layer Expedition (ABLE 3A) flight region are constructed using photochemical model statistics based on aircraft observations. A Lagrangian model is used to reconstruct the photochemical history of two aged biomass fire plumes sampled by the ABLE 3A aircraft. It is shown that anthropogenic influence on O3 levels in the Arctic may manifest itself not by long-range transport of pollution-derived O3, but rather by a decrease of the regional photochemical sink due to the presence of small amounts of NO(x). The low concentrations of NO(x) measured in ABLE 3A were sufficient to reduce the rate of photochemical loss appreciably relative to a NO(x)-free atmosphere, thus increasing the O3 lifetime. It is shown that decomposition of PAN can account for most of the NO(x) measured below 4-km altitude, but for only 20 percent at 6-km altitude. A lifetime of 29 days is estimated for NO(y) in the ABLE 23A flight region.

Jacob, D. J.↗
NASA NTRS1993

Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors

Statistical modeling and evaluation of the performance of obstacle detection systems for Unmanned Ground Vehicles (UGVs) is essential for the design, evaluation, and comparison of sensor systems. In this report, we address this issue for imaging range sensors by dividing the evaluation problem into two levels: quality of the range data itself and quality of the obstacle detection algorithms applied to the range data. We review existing models of the quality of range data from stereo vision and AM-CW LADAR, then use these to derive a new model for the quality of a simple obstacle detection algorithm. This model predicts the probability of detecting obstacles and the probability of false alarms, as a function of the size and distance of the obstacle, the resolution of the sensor, and the level of noise in the range data. We evaluate these models experimentally using range data from stereo image pairs of a gravel road with known obstacles at several distances. The results show that the approach is a promising tool for predicting and evaluating the performance of obstacle detection with imaging range sensors.

Matthies, Larry↗
NASA NTRS2000

Operational Implementation of a 2-Hour Prebreathe Protocol for International Space Station

Procedures, equipment, and analytical techniques were developed to implement the ground tested 2-hour protocol in-flight operations. The methods are: 1) The flight protocol incorporates additional safety margin over the ground tested protocol. This includes up to 20 min of additional time on enriched O2 during suit purge and pressure check, increased duration of extravehicular activity (EVA) preparation exercise during O2 prebreathing (up to 90 min vs; the tested 24 min), and reduced rates of depressurization. The ground test observations were combined with model projections of the conservative measures (using statistical models from Duke University and NASA JSQ to bound the risk of Type I and Type II decompression sickness (DCS). 2) An inflight exercise device using the in-flight ergometer and elastic tubes for upper body exercise was developed to replicate the dual cycle exercise in the ground trials. 3) A new in-flight breathing system was developed and man-tested. 4) A process to monitor inflight experience with the protocol, including the use of an in-suit Doppler bubble monitor when available, was developed. The results are: 1) The model projections of the conservative factors of the operational protocol were shown to reduce the risk of DCS to levels consistent with the observations of no DCS to date in the shuttle program. 2) Cross over trials of the dual cycle ergometer used in ground tests and the in-flight exercise system verified that02consumption and the % division of work between upper and lower body was not significantly different at the p= 0.05 level. 3) The in-flight breathing system was demonstrated to support work rates generating 75% O2(max) in 95 percentile subjects. 4) An in-flight monitoring plan with acceptance criteria was put in place for the 2-hour prebreathe protocol. And the conclusions are: The 2-hour protocol has been approved for flight, and all implementation efforts are in place to allow use of the protocol as early as flight ISS 7A, now scheduled in November of 2000.

Waligora, James M.↗