SEARCH · Search NASA

Results for “TRAINING - UNIVERSITIES”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Attribute-Guided Adversarial Training for Robustness to Natural Perturbations

We report while existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real world settings. In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknown degree of rotation) may be known. We consider a setup where robustness is expected over an unseen test domain that is not i.i.d. but deviates from the training domain. While this deviation may not be exactly known, its broad characterization is specified a priori, in terms of attributes. We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space, without having access to the data from the test domain. Our adversarial training solves a min-max optimization problem, with the inner maximization generating adversarial perturbations, and the outer minimization finding model parameters by optimizing the loss on adversarial perturbations generated from the inner maximization. We demonstrate the applicability of our approach on three types of naturally occurring perturbations --- object-related shifts, geometric transformations, and common image corruptions. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations. We demonstrate the usefulness of the proposed approach by showing the robustness gains of deep neural networks trained using our adversarial training on MNIST, CIFAR-10, and a new variant of the CLEVR dataset.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Fiats: Functional inference and training for surrogates

Fiats provides a platform for research on the training and deployment of neural-network surrogate models for computational science. Fiats also supports exploring, advancing, and combining functional, object-oriented, and parallel programming patterns in Fortran 2023. As such, the Fiats name has dual expansions: “Functional Inference And Training for Surrogates” or “Fortran Inference And Training for Science.” Fiats inference and training procedures are pure and therefore satisfy a language constraint imposed on procedure invocations inside Fortran’s parallel loop construct: do concurrent. Furthermore, the Fiats training procedures are built around a do concurrent parallel reduction. Several compilers can automatically parallelize do concurrent on Central Processing Units (CPUs) or Graphics Processing Units (GPUs). Fiats thus aims to achieve performance portability through standard language mechanisms.

Rouson, Damian [Lawrence Berkeley National Laborat↗

DOE OSTI2020

UTrain Manual for Training Administrators

This Institutional Training Administrative Procedure provides the business rules that must be followed when working in UTrain. As commercial off-the-shelf software with customizations, UTrain cannot address all the implementation business requirements, and thus, administrative business rules and processes were defined and documented. Many business rules were incorporated into the UTrain training materials. UTrain is the Laboratory’s official learning management system. UTrain is the system of record for all Laboratory training, qualification, certification, and worker authorization. All training at LANL must be documented in UTrain. The detailed steps of how to operate UTrain are contained in the UTrain courses and training manuals and in the UTrain job aids, not in this document.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

Metaevaluation Final Report: Sandia National Laboratories Recorded Information Management Training Program Evaluation

For my internship project, I chose to evaluate the training program for the Sandia National Laboratories (SNL) Recorded Information Management (RIM) Program. The purpose of the evaluation was to identify: Where the current training meets federal and corporate requirements and if not, what gaps exist; If the training is accessible in a consistent location; If the training contains a consistent message about the responsibility of managing documents; If the training is organized by records management subject areas.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

TA-55 Forensic Support Operations Cross-Training Exercise

As part of the annual FBI/LANL training schedule, a joint training exercise was conducted during the week of 10FEB2020 with FBI HEAT members and LANL Fissile Material Handlers (FMHs). The joint training exercise was conducted in a non-radiological area to reduce cost and ensure the test object would not be contaminated. The exercise test object was fabricated by LANL using surrogate materials to represent an object containing Special Nuclear Material (SNM). Working with Jim Blankenship and Kevin Swearingen, functional requirements of the test object included the following: unclassified, disassembly (to include screws), and moderately heavy. To meet the objectives, the test object was fabricated by welding a custom aluminum box (8” x 8” x 12”) with a lid which was secured by four (4) screws. The custom box housed a track and field shot put (16 lbs.) that was anchored by a hose clamp. The intent of the training was to disassemble the test object and conduct traditional forensic determinations on the parts. This lessons learned report documents all of the associated aspects related to the joint training effort.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

EV Champion Training Webinar 1: ZEV and EV Charging Fundamentals [Slides]

The Electric Vehicle (EV) Champion Training Series, hosted by the National Renewable Energy Laboratory (NREL), is tailored for fleet managers, facility managers, and other stakeholders involved in the deployment of EVs and charging stations. This series equips participants with the skills and knowledge necessary to become subject matter experts in EV implementation. This is the first training in a four-part series and serves as an introductory training. This training covers fundamental topics such as EV and charging technology, utility basics, and financial considerations for EVs. Additionally, this session introduces the Federal Fleet ZEV Ready Center framework, a one-stop location for federal fleet electrification resources. Participants will gain a solid foundation to support the effective deployment and management of EVs and their infrastructure. Visit the Federal Fleet ZEV Ready Center website to learn more about the ZEV Ready Center process. EV Champion Training Webinar 1: ZEV and EV Charging Fundamentals will enable attendees to: Identify EV and EV charging technology fundamentals; Recognize EV market and GSA Schedule options; Identify utility basics, incentives, and rates as they relate to EV charging; and Identify methods to calculate life cycle costs for EVs and gasoline vehicles.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2025

EV Champion Training Webinar 2: ZEV and EV Charging Planning [Slides]

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Building Datasets and Training Methods for ML Based Magnet Quench Detection

Detecting quenches in superconducting (SC) magnets during training is a challenging process that involves capturing physical events that occur at different frequencies and appear as various signal features. These events may be correlated across instrumentation type, thermal cycle, and ramp. These events together build a more complete picture of continuous processes occurring in the magnet, and may allow us to flag potential precursors for quench detection. We present our work on building an automatic machine learning (ML) based quench detection system. We build upon our existing work on unsupervised auto-encoders for acoustic sensors and quench antenna (QA) by first establishing a supervised ML training pipeline. We show the results of an event tagging, analysis, and simulation framework on our QA and acoustic data which are used concurrently to build a training dataset for a supervised implementation. We then show how this supervised training can be used as a prior in a semi-supervised framework and compare this to the unsupervised neural network auto-encoder performance.This allows us to have a more concrete understanding of the performance of our algorithms relative to physical events occurring in the magnet, and also provides a baseline software tool to generically evaluate our quench prediction autoencoders under completely unsupervised, supervised, and semi-supervised training conditions.

Khan, Maira [Fermilab]↗

DOE OSTI2025

Virtual Reality for Shoot/No-Shoot Decision Training in Law Enforcement: A Literature Review and Research Agenda

Virtual reality (VR) can materially improve “shoot / no-shoot” (SNS) training by giving officers realistic, repeatable practice making high-stakes decisions under pressure. Traditional tools—live-fire ranges and video simulators—build basics, but they cannot adapt to each officer in real time or fully mirror the complexity of the field. VR closes that gap by creating immersive scenarios that are safer, more flexible, easier to scale across units, and able to capture objective performance data. SNS decisions are not just about marksmanship; they rely on perception, judgment, memory, and the ability to hold fire when a threat is uncertain. Effective training therefore needs realism, decision complexity, and branching outcomes that reflect the true consequences of choices. These elements strengthen recognition of hostile intent while reducing false positives and building the self-control required in ambiguous situations. VR brings specific advantages: dynamic environments, full-body interaction, and the ability to measure performance with precision—enabling targeted feedback and better transfer of learning to the street. At the same time, responsible deployment must address scenario quality (credible environments and behaviors), lawful decision models, and user wellbeing (appropriate stress levels, comfort, and safety). Sandia’s VIPER Lab is positioned to lead this work. The team combines human-performance science, AI/ML, and VR/AR development with a deep equipment bench (e.g., omnidirectional treadmill, eye-tracking, haptics, multiple HMDs). This ecosystem supports building and validating next-generation SNS training that is immersive, measurable, and trustworthy. Bottom line: Investment in VR-enabled SNS training that blends evidence-based design with careful validation and legal safeguards is expected to pay off in safer, more consistent decision-making and improved community trust, delivered through training that is practical to deploy at scale.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2025

Datasets for Custom-trained Machine-learning Interatomic Potentials: Nitric Acid Aqueous Solution

This dataset was generated using an iterative active learning strategy with the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials (MLIPs) for aqueous nitric acid. Each active-learning cycle consisted of three stages: (1) training, (2) exploration, and (3) labeling. The initial training set comprised approximately 800 randomly selected configurations from a previous study by Lewis et al. (https://doi.org/10.1021/jp205510q), which investigated nitric acid solutions at 2, 3, 4, and 5 mol/L. For all configurations, single-point calculations of atomic forces and total energies were performed at the quantum density functional theory BLYP-D2 and PBE-D3 levels of theory using the CP2K Quickstep module. Valence electrons were treated explicitly, while core electrons on all atoms were represented by norm-conserving Goedecker–Teter–Hutter (GTH) pseudopotentials. Long-range dispersion interactions were accounted for using Grimme dispersion corrections. Wave functions were expanded in a mixed Gaussian-and-plane-wave scheme using TZV2P-MOLOPT basis sets for all elements and an 800 Ry auxiliary plane-wave cutoff for the electron density. Self-consistent field convergence was accelerated using orbital transformation and Direct Inversion in the Iterative Subspace, with a convergence threshold of 10^{-6}. All single-point calculations were carried out in periodic orthorhombic cells whose dimensions match those of the molecular configurations sampled from earlier trajectories. The CELL_REF keyword in CP2K was used to define a fixed reference cell, ensuring consistency in the reference data used for MLIP training, particularly when cell fluctuations are present in NpT simulations. The resulting high-fidelity energies and forces constitute the ground-truth labels used to train the MLIPs contained in this dataset.

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗

DOE OSTI2023

Systems and methods for active learning from sparse training data

A method for active learning using sparse training data can include training a machine learning model using less than ten first training data points to generate a candidate machine learning model. The method can include performing a Monte Carlo process to sample one or more first outputs of the candidate machine learning model. The method can include testing the one or more first outputs to determine if each of the one or more first outputs satisfy a respective convergence condition. The method can include, responsive to at least one first output not satisfying the respective convergence condition, training the candidate machine learning model using at least one second training data point corresponding to the at least one first output. The method can include, responsive to the one or more first outputs each satisfying the respective convergence condition, outputting the candidate machine learning model.

Sankaranarayanan, Subramanian↗

DOE OSTI2022

CFD-trained ANN Model for Approximating Near-occupant Condition in Real-time Simulations

The main drawback of Computational Fluid Dynamics (CFD) simulations has been the time and resource consuming nature which is not suitable for real-time applications. In this work, we first generated numerous CFD models of a given indoor space to obtain airspeed, temperature, and mean radiant temperature near an occupant as training data. Several artificial neural networks (ANN) models were trained using this CFD simulated data to approximate near real-time environmental conditions for a given occupant. This trained ANN model approach is a part of a real-time simulation of building operations using a combination of software and real hardware (HVAC equipment) approaches. The preliminary results suggest that the CFD- generated training data and the trained ANN model can accurately approximate such conditions in a real-time application, a method that has great potential in building simulation and building digital twin areas of research.

Zhang, Yun↗

DOE OSTI2023

Nuclear Criticality Safety Training: Needs and Efforts [Slides]

This presentation on Nuclear Criticality Safety Training: Needs and Efforts covers several points. This presentation starts with a quick overview of Nuclear Criticality Safety (NCS) Training. Then it lists current and ongoing training with a focus on the training in progress. This presentation then concludes with a look at supporting efforts.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

Importance of Hands-on Training within the Nuclear Industry

Almost all work performed within the nuclear industry requires some form of training, regardless of an individual’s education. While many of the hazards that exist in nuclear operations are present in other industries (e.g., industrial safety), nuclear operations have the unique hazards of radiation and inadvertent criticality. Therefore, these topics require additional training for individuals performing hands-on work or accessing those work areas. Many methods are available to train people including lectures (either with an instructor or automated video), reading procedures/policies, hands-on demonstrations, and on-the-job training.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem

Some research topics belonging to classification problems in the nuclear industry, such as fault diagnosis and accident identification, can be solved by feature extraction and subsequent application of statistical machine learning classifiers. Recently, deep neural network-based methods with automatic feature extraction and high accuracy have gained wide attention. They usually require large-scale training data, however, plant fault or accident data are scarce or difficult to obtain. Here this paper proposes a convolutional network (CNN)-based transfer learning method to solve this problem. The network's shallow layer is derived from a pre-trained CNN based on the ImageNet database to automatically extract features, and the deep layer is customized to match the classification problem. Data in non-image formats are converted to image formats and subsequently used to train the network. Case studies of rotating machines fault diagnosis show that the proposed method requires only limited training data to achieve high accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

A mathematical approach to using the forgetting curve to evaluate experience and training factors in human reliability analysis

Traditional human reliability analysis (HRA) methods have difficulty dealing with the dynamic nature of factors such as time and rely on static and expert-judgment-based assessments of performance-shaping factors (PSFs) across limited levels. In this study, we introduce a mathematical approach for dynamically evaluating the experience and training PSF. Our proposed method integrates the psychological concept of the “forgetting curve” to evaluate how PSFs are impacted by the number of trainings and the time elapsed since training. To confirm the validity of the model, we provide experimental data fitted by identifying the quantitative relationship between training and human performance. This research enables dynamic and objective assessments, thus reducing reliance on subjective expert judgment and improving the accuracy of HRA.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Strategies and Approaches for Developing Hands-On Training for Cold Climate Heat Pumps

The International Center for Appropriate and Sustainable Technology (ICAST) is in the process of implementing a program funded by the US Department of Energy (DOE) in which they are providing cold climate heat pump (CCHP) curriculum and training to HVAC workers, ranging from entry to experienced levels. They have coordinated with Santa Fe Community College (SFCC) to develop online curricula, and through their involvement with the DOE Better Buildings Workforce Accelerator, they requested technical assistance from the National Renewable Energy Laboratory (NREL) to help them develop programs that incorporate hands-on and on-the-job skills training and job placement services in coordination with local employers. This report offers a step-by step-overview of the process for developing a new workforce development and hands-on training program in a new region, outlining practices to help understand the market, and to effectively recruit, train, and connect people to HVAC jobs installing cold climate heat pumps.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Current Status of the DOE/NNSA Nuclear Criticality Safety Program Hands-on Criticality Safety Training Courses [Slides]

The NCSP training and education program has been conducted since 2011 and has trained over 591 students. 388 students have been trained in the 2-week hands-on course for practitioners and 203 students have been trained in the 1-week manager courses. The course is continuously improved using lessons-learned from the prior year, and new course content is added as necessary. Special courses are developed and offered periodically at the discretion of the NCSP manager. The updated manager course for CSOs was successfully piloted at NCERC in June 2020 and was successfully piloted at Sandia in April 2022. Students interested in taking the courses should visit the DOE NCSP website for additional information about all course offerings and for information about course registration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗