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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Sequential Training ToolKit - STTK

Sequential Training ToolKit, or STTK, is a software toolkit plugin for the Unity development platform that enables software developers to easily develop structured Sequential Training applications for gaming and extended reality (XR) environments. STTK is architected to easily import the toolkit as a plugin for the Unity platform and then interact with the graphical user interface (GUI) for drag-and-drop functionality. STTK also includes easy add-on plugins for Prefabs and auto-builds a State Machine. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-3059 O

Klein, BrandonThorin↗
DOE OSTI2021

Multilevel Training of Neural Networks

MTNN is a software library for applying multilevel algorithms on training neural networks. Based on PyTorch, it is designed to be an extensible framework to enable researchers to implement multilevel training algorithms on a variety of neural network models. This release includes a culmination of preliminary work in applying the multilevel Full Approximation Scheme technique on fully connected and convolutional neural networks to solve PDE problems (pending publication submission).

Li, Ruipeng↗
DOE OSTI2024

Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset

Abstract Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic—primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a reevaluation of the top-performing candidate models postretraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data—a few thousand samples—in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135× speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Comparing Designed Training Sets to Optimize Multivariate Regression Models for Pr, Nd, and Nitric Acid Using Spectrophotometry

Chemometric regression models were developed for the quantification of praseodymium (Pr, 0–1000 µg/mL), neodymium (Nd, 0–1000 µg/mL), and nitric acid (HNO 3 , 0.1–5 M) using spectrophotometry. Designed calibration sets were composed of 20 samples each: 10 model points and 10 lack-of-fit (LOF) points. The D-optimal designs effectively minimized the number of samples required to build models, and each design resulted in similar prediction performance, suggesting that statistical design of experiments can provide a reliable framework for selecting training set samples in three-variable systems. Partial least squares regression (PLSR) models were validated against a one-factor-at-a-time validation set composed of 125 samples (three variables, five levels). The top PLS-1 models resulted in average percent root mean square error of prediction error values of 3.5%, 1.7%, and 1.2% for Pr(III), Nd(III), and HNO 3 , respectively. Power set augmentations of the model and LOF samples were investigated to optimize the number of training set samples. PLSR models built using just required model points (10) had similar predictive capabilities as models including the LOF points (20) but with fewer samples. The number of validation samples was also varied systematically to learn how many samples are needed to validate regression models. This work addresses long-standing questions in the field of chemometrics to help make this approach amenable to the near-real-time quantification of hazardous species in remote settings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Graduate Training at the Interface of Computational and Experimental Biology: An Outcome Report from a Partnership of Volunteers between a University and a National Laboratory

Leading voices in the biological sciences have called for a transformation in graduate education leading to the PhD degree. One area commonly singled out for growth and innovation is cross-training in computational science. In 1998, the University of Tennessee (UT) founded an intercollegiate graduate program called the UT-ORNL Graduate School of Genome Science and Technology in partnership with the nearby Oak Ridge National Laboratory. Here, we report outcome data that attest to the program’s effectiveness in graduating computationally enabled biologists for diverse careers. Among 77 PhD graduates since 2003, the majority came with traditional degrees in the biological sciences, yet two-thirds moved into computational or hybrid (computational–experimental) positions. We describe the curriculum of the program and how it has changed. We also summarize how the program seeks to establish cohesion between computational and experimental biologists. This type of program can respond flexibly and dynamically to unmet training needs. In conclusion, this study from a flagship, state-supported university may serve as a reference point for creating a stable, degree-granting, interdepartmental graduate program in computational biology and allied areas.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effectiveness of Deep Learning Trained on SynthCity Data for Urban Point-Cloud Classification

3D object recognition is one of the most popular areas of study in computer vision. Many of the more recent algorithms focus on indoor point clouds, classifying 3D geometric objects, and segmenting outdoor 3D scenes. One of the challenges of the classification pipeline is finding adequate and accurate training data. Hence, this article seeks to evaluate the accuracy of a synthetically generated data set called SynthCity, tested on two mobile laser-scan data sets. Varying levels of noise were applied to the training data to reflect varying levels of noise in different scanners. The chosen deep-learning algorithm was Kernel Point Convolution, a convolutional neural network that uses kernel points in Euclidean space for convolution weights.

Geology↗
DOE OSTI2025

Reference Shapefiles and Pre-trained Random Forest Classification Models for Detecting Aufeis on the North Slope of Alaska in Landsat Imagery

This dataset provides shapefiles and trained machine learning models used for aufeis detection at four sites on the North Slope of Alaska. It includes reference data for evaluating Landsat-based detection methods, supporting research on remote sensing approaches for identifying aufeis. The ReferenceData folder contains ArcGIS shapefiles of semi-automated land cover classifications for 217 Landsat Collection 2 images, categorizing pixels into six classes: aufeis, snow, ground, none, water, and cloud. The SiteBuffers.zip file includes 10-kilometer buffer shapefiles defining regions of interest around four aufeis fields (Canning21, FH1, Firth, and Kuparuk), used to test three detection techniques. Additionally, the TrainedRFModels folder contains six pre-trained Scikit-Learn Random Forest classifiers (100 trees, max depth = 30) designed to predict aufeis presence in Landsat Collection 2 Surface Reflectance images using Red, Blue, SWIR2, NDVI, and NDWI bands. This dataset supports the development and validation of remote sensing methods for mapping aufeis in Arctic environments.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

The Science Training Program for Young Italian Physicists and Engineers at Fermilab

The summer training program for Italian undergraduate and graduate students at the Department of Energy (DOE) laboratory of Fermilab (Batavia, Illinois, USA) started in 1984 as a 2 month training program for Italian undergraduate students in physics of the Istituto Nazionale di Fisica Nucleare (INFN) collaborating in the Collider Detector experiment (CDF) at the Fermilab Tevatron proton - antiproton collider. While in 1984 the program involved only 4 physics students from the University of Pisa, in the following years it rapidly grew in scope and size under the management of the Cultural Association of Italians at Fermilab (CAIF). With an average number of 30 trainees/year reached in the last few years, the total number of Italian students hosted at Fermilab since 1984 has exceeded 500 units. Since 2015 the program has been included in the portfolio of the summer courses of the University of Pisa, which acknowledges 6 ECTS to the interns.

Barzi, Emanuela↗
DOE OSTI2011

Contractor Sales Training: Providing the Skills Necessary to Sell Comprehensive Home Energy Upgrades

Many comprehensive home energy efficiency programs rely on contractors as the customer-facing ‘front line’ to sell energy improvements. Adding sales skills to contractors’ existing technical expertise is key to converting more assessments into comprehensive home energy upgrades. Leading programs recognize the need to support contractors with sales and business training to help them succeed and to support the growth of the home performance industry for the long term. A number of contractor sales training efforts are emerging, including some programs that are seeing encouraging early results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Energy Commissioning Agent/Auditor Online Training Program

The original objective of this project was to create a comprehensive training and deployment program - suitable for in-person delivery in the New York metro region – focused on educating building commissioning authorities and energy auditors. The NJIT Center for Building Knowledge was to have sole responsibility for this curriculum development and deployment plan effort. Very early in the project timeline, however, the scope of the project evolved – with DOE and NETL approval - to become a collaborative effort between NJIT and Portland Energy Conservation, Inc. (PECI). The structure of this collaboration involved PECI taking the lead on developing a comprehensive curriculum program suitable for in-person delivery, and NJIT taking the lead on turning this curriculum into a comprehensive online training program that would complement – and extend the reach of – the in-person curriculum.

Evans, Deane↗
DOE OSTI2013

Stormwater Management Plan for the Arden Hills Army Training Site, Arden Hills, Minnesota

This stormwater management plan focuses on the cantonment and training areas of the Arden Hills Army Training Site (AHATS). The plan relates the site stormwater to the regulatory framework, and it summarizes best management practices to aide site managers in promoting clean site runoff. It includes documentation for a newly developed, detailed model of stormwater flow retention for the entire AHATS property and adjacent upgradient areas. The model relies on established modeling codes integrated in a U.S. Department of Defense-sponsored software tool, the Watershed Modeling System (WMS), and it can be updated with data on changes in land use or with monitoring data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Carbon Capture and Storage in the Permian Basin, a Regional Technology Transfer and Training Program

The Permian Basin Carbon Capture, Utilization and Storage (CCUS) Training Center was one of seven regional centers formed in 2009 under the American Recovery and Reinvestment Act of 2009 and managed by the Department of Energy. Based in the Permian Basin, it is focused on the utilization of CO 2 Enhanced Oil Recovery (EOR) projects for the long term storage of CO 2 while producing a domestic oil and revenue stream. It delivers training to students, oil and gas professionals, regulators, environmental and academia through a robust web site, newsletter, tech alerts, webinars, self-paced online courses, one day workshops, and two day high level forums. While course material prominently features all aspects of the capture, transportation and EOR utilization of CO 2 , the audience focus is represented by its high level forums where selected graduate students with an interest in CCUS interact with Industry experts and in-house workshops for the regulatory community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Sustainable Hydropower in Lower Mekong Countries: Technical Assessment and Training Travel Report Thailand

The U.S. Agency for International Development (USAID), through their partnership with the U.S. Department of the Interior (DOI), requested the support of Oak Ridge National Laboratory (ORNL) to provide specialized technical assistance as part of the Smart Infrastructure for the Mekong (SIM) Program in Thailand. Introduced in July 2013 by U.S. Secretary of State John Kerry, SIM is a U.S. Government Inter-Agency program that provides Lower Mekong partner countries with targeted, demand-driven technical and scientific assistance to support environmentally sound, climate conscious and socially equitable infrastructure, clean energy development, and water resources optimization. The U.S. Government is committed to supporting sustainable economic development within the region by providing tools, best practices, technical assistance, and lessons learned for the benefit of partner countries. In response to a request from the Electricity Generating Authority of Thailand (EGAT), a SIM project was developed with two main activities: 1) to promote hydropower sustainability and efficiency through technical assessment training at two existing hydropower assets in Thailand, and 2) the design and implementation of one national and two or three regional science and policy workshops, to be co-hosted with EGAT, to build common understanding of and commitment to environmental and social safeguards for Mekong Basin hydropower projects. The U.S. Department of Energy (DOE) is leading the technical assessment (Activity 1), and has contracted ORNL to provide expert technical assistance focused on increasing efficiency at existing projects, with the goal of increasing renewable energy generation at little to no capital cost. ORNL is the leading national laboratory in hydropower analysis, with a nationally recognized and highly qualified team of scientists addressing small to large-scale systems (basin-, regional-, and national-scale) energy generation optimization analysis for DOE. The mission of the ORNL Water Power Program is to develop technologies, decision-support tools, and methods of analysis that enable holistic management of water-dependent energy infrastructure and natural resources in support of the DOE Energy Efficiency and Renewable Energy Office (DOE-EERE), Federal hydropower agencies, Federal Energy Regulatory Commission (FERC), Nuclear Regulatory Commission (NRC), energy producers, and other entities. In support of SIM, ORNL completed technical assessments of two hydropower plants owned and operated by the Electricity Generating Authority of Thailand (EGAT): Vajiralongkorn (VRK), with an installed capacity of 300 MW, and Rajjaprabha (RPB), with an installed capacity of 240MW. Technical assessment is defined as the assessment of hydropower operation and performance, and the identification of potential opportunities for performance improvement through plant optimization. At each plant, the assessment included an initial analysis of hydropower operating and performance metrics, provided by dam owners. After this analysis, ORNL engaged with the plant management team in a skills exchange, where best practices, operational methods, and technical challenges were discussed. The technical assessment process was outlined to plant management followed by a presentation of preliminary results and analysis based on 50 days of operational data. EGAT has agreed to provide a full year of operational data so a complete and detailed assessment that captures seasonal variability can be completed. The results of these assessments and discussions will be used to develop a set of best practices, training, and procedure recommendations to improve the efficiency of the two assessed plants.

13 HYDRO ENERGY↗