SEARCH · Engineering Papers

Results for “Active learning”

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 523 records · Page 29

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation

Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn–Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structures and chemical bonds of such systems. In the complete active space self-consistent field (CASSCF) method, one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. Here, we present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a “black-box” mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1992

Automatic learning rate adjustment for self-supervising autonomous robot control

Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

Arras, Michael K.↗

NASA NTRS2002

Issues in Informal Education: Event-Based Science Communication Involving Planetaria and the Internet

For the past four years the Science Directorate at Marshall Space Flight Center has carried out a diverse program of science communication through the web resources on the Internet. The program includes extended stories about NAS.4 science, a curriculum resource for teachers tied to national education standards, on-line activities for students, and webcasts of real-time events. Events have involved meteor showers, solar eclipses, natural very low frequency radio emissions, and amateur balloon flights. In some cases broadcasts accommodate active feedback and questions from Internet participants. We give here, examples of events, problems, and lessons learned from these activities.

Adams, M.↗

DOE OSTI2021

Active anomaly detection for time-domain discoveries

Aims. We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. Results. We show that, in the real data scenario , AAD was able to identify ~80% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

A core lighting curriculum for university students and lighting professionals

Here, in collaboration with a group of lighting professionals, learning outcomes were defined, prioritized, organized and mapped to a three-course sequence of lighting courses within a Bachelor of Science in Architectural Engineering degree programme. Syllabi and educational exercises were developed to support the learning outcomes – including classroom activities, homework assignments and design projects. The learning exercises balance the technical foundations of applied illuminating engineering with the artistic aspects of applied lighting design and are intended to promote significant and lasting learning by providing students with education that is useful and relevant to current lighting practice. The process for identifying and prioritizing lighting content is described, a process that could be adapted by other lighting educators to other pedagogical contexts. The syllabi and learning exercises are disseminated for reuse or adaptation, or for self-study by independent learners.

42 ENGINEERING↗

DOE OSTI2023

Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors

Abstract Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to find the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with specific surface areas > 4000 m 2 /g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm 2 of electrode mass loading that exhibits a high specific capacitance of 610 F/g in 1 M H 2 SO 4 . This result approaches the specific capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2001

Issues in Informal Education: Event-Based Science Communication Involving Planetaria and the Internet

For the last several years the Science Directorate at Marshall Space Flight Center has carried out a diverse program of Internet-based science communication. The program includes extended stories about NASA science, a curriculum resource for teachers tied to national education standards, on-line activities for students, and webcasts of real-time events. The focus of sharing real-time science related events has been to involve and excite students and the public about science. Events have involved meteor showers, solar eclipses, natural very low frequency radio emissions, and amateur balloon flights. In some cases broadcasts accommodate active feedback and questions from Internet participants. Panel participation will be used to communicate the problems and lessons learned from these activities over the last three years.

Adams, Mitzi L.↗

NASA NTRS2005

Engineering Lessons Learned and Systems Engineering Applications

Systems Engineering is fundamental to good engineering, which in turn depends on the integration and application of engineering lessons learned. Thus, good Systems Engineering also depends on systems engineering lessons learned from within the aerospace industry being documented and applied. About ten percent of the engineering lessons learned documented in the NASA Lessons Learned Information System are directly related to Systems Engineering. A key issue associated with lessons learned datasets is the communication and incorporation of this information into engineering processes. As part of the NASA Technical Standards Program activities, engineering lessons learned datasets have been identified from a number of sources. These are being searched and screened for those having a relation to Technical Standards. This paper will address some of these Systems Engineering Lessons Learned and how they are being related to Technical Standards within the NASA Technical Standards Program, including linking to the Agency's Interactive Engineering Discipline Training Courses and the life cycle for a flight vehicle development program.

Gill, Paul S.↗

NASA NTRS2007

TRL Assessment of Solar Sail Technology Development Following the 20-Meter System Ground Demonstrator Hardware Testing

The NASA In-Space Propulsion Technology (ISPT) Projects Office has been sponsoring 2 separate, independent system design and development hardware demonstration activities during 2002-2005. ATK Space Systems of Goleta, CA was the prime contractor for one development team and L'Garde, Inc. of Tustin, CA was the prime contractor for the other development team. The goal of these activities was to advance the technology readiness level (TRL) of solar sail propulsion from 3 towards 6 by the year 2006. Component and subsystem fabrication and testing were completed successfully, including the ground deployment of 10-meter and 20-meter ground demonstration hardware systems under vacuum conditions. The deployment and structural testing of the 20-meter solar sail systems was conducted in the 30 meter diameter Space Power Facility thermal-vacuum chamber at NASA Glenn Plum Brook in April though August, 2005. This paper will present the results of the TRL assessment following the solar sail technology development activities associated with the design, development, analysis and testing of the 20-meter system ground demonstrators. Descriptions of the system designs for both the ATK and L'Garde systems will be presented. Changes, additions and evolution of the system designs will be highlighted. A description of the modeling and analyses activities performed by both teams, as well as testing conducted to raise the TRL of solar sail technology will be presented. A summary of the results of model correlation activities will be presented. Finally, technology gaps identified during the assessment and gap closure plans will be presented, along with "lessons learned", subsequent planning activities and validation flight opportunities for solar sail propulsion technology.

Montgomery, Edward E.↗

NASA NTRS2007

TRL Assessment of Solar Sail Technology Development Following the 20-Meter System Ground Demonstrator Hardware Testing

Young, Roy M.↗

DOE OSTI2023

Probe microscopy is all you need *

We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tremendous progress achieved by machine learning (ML) and artificial intelligence over the last decade has been largely achieved via the utilization of static data sets, from the paradigmatic MNIST to the bespoke corpora of text and image data used to train large models such as GPT3, DALL·E and others. However, it is now recognized that continuous, minute improvements to state-of-the-art do not necessarily translate to advances in real-world applications. We argue that a promising pathway for the development of ML methods is via the route of domain-specific deployable algorithms in areas such as electron and scanning probe microscopy and chemical imaging. This will benefit both fundamental physical studies and serve as a test bed for more complex autonomous systems such as robotics and manufacturing. Favorable environment characteristics of scanning and electron microscopy include low risk, extensive availability of domain-specific priors and rewards, relatively small effects of exogenous variables, and often the presence of both upstream first principles as well as downstream learnable physical models for both statics and dynamics. Recent developments in programmable interfaces, edge computing, and access to application programming interfaces (APIs) facilitating microscope control, all render the deployment of ML codes on operational microscopes straightforward. We discuss these considerations and hope that these arguments will lead to create novel set of development targets for the ML community by accelerating both real world ML applications and scientific progress.

47 OTHER INSTRUMENTATION↗

NASA NTRSDate not supplied

Exploring the Capabilities of a Machine Learning Algorithm to Detect Space Weather-Significant Emerging Active Regions

Active regions are a source of various phenomena responsible for Space Weather disturbances; therefore, developing a technology for early warning about upcoming magnetic activity is crucial to mitigate its impact. However, observational limitations and the high nonlinearity of processes associated with the accumulation of magnetic flux and its interaction with the surrounding plasma during the emergence through the convection zone make early activity detection a challenging problem. To address these challenges, we developed a physics-driven machine learning model that allows us to detect active regions (ARs) before they become visible on the solar surface by analyzing the power spectra of acoustic oscillations observed by the SDO/HMI instrument. This study is based on a time series of Doppler shift maps of 31x31-degree areas tracked with the Carrington rotation rate for four days before and after the emergence. The Doppler shift time series are processed into the oscillation power maps for four frequency ranges and accompanied by line-of-sight magnetograms and the continuum intensity maps from SDO/HMI. The resulting data are converted into a 1D time series representing the mean temporal variations of these quantities. The redacted time series are used as input to predict AR emergence using the Long Short Term Memory (LSTM) method. The training of the LSTM model is based on 40 ARs, which includes an independent analysis for each sub region that exhibits AR emergence or remains quiet. The emergence of magnetic flux (defined as a decrease of the continuum intensity) was detected with the developed LSTM algorithm from 5 to 48 hours before the reported time by NOAA. The developed model is capable of pointing to the time and location of active region formation. In this presentation, we discuss reasons that impact how early in advance the model can identify the upcoming activity and the possibility of improving the current predictive skills and steps to transition to the operational forecast.

Heliophysics↗

DOE OSTI2021

Social Learning and Solar Photovoltaic Adoption

Growing literature points to the effectiveness of leveraging social interactions and nudges to spur adoption of prosocial behaviors. This study investigates a large-scale behavioral intervention designed to actively leverage social learning and peer interactions to encourage adoption of residential solar photovoltaic systems. Municipalities choose a solar installer offering group pricing and undertake an informational campaign driven by volunteer ambassadors. We find a causal treatment effect of 37 installations per municipality from the campaigns and no evidence of harvesting or persistence. The intervention also lowers installation prices. Randomized controlled trials based on the intervention show that selection into the program is important, whereas group pricing is not. Our results suggest that the program provided economies of scale and lowered consumer acquisition costs, leading to low-cost emission reductions. This paper was accepted by Matthew Shum, marketing.

Business & Economics↗

NASA NTRS2013

'How To' Clean Room Video

One of the projects that I am completing this summer is a Launch Services Program intern 'How to' set up a clean room informational video. The purpose of this video is to go along with a clean room kit that can be checked out by employees at the Kennedy Space Center and to be taken to classrooms to help educate students and intrigue them about NASA. The video will include 'how to' set up and operate a clean room at NASA. This is a group project so we will be acting as a team and contributing our own input and ideas. We will include various activities for children in classrooms to complete, while learning and having fun. Activities that we will explain and film include: helping children understand the proper way to wear a bunny suit, a brief background on cleanrooms, and the importance of maintaining the cleanliness of a space craft. This project will be shown to LSP management and co-workers; we will be presenting the video once it is completed.

McCarty, Kaley Corinne↗

DOE OSTI2021

Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes

Abstract Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active‐source seismic data, which probe asperity‐scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P‐wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to “learn” about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics‐informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.

58 GEOSCIENCES↗

DOE OSTI2020

Graph interpolating activation improves both natural and robust accuracies in data-efficient deep learning

Improving the accuracy and robustness of deep neural nets (DNNs) and adapting them to small training data are primary tasks in deep learning (DL) research. In this paper, we replace the output activation function of DNNs, typically the data-agnostic softmax function, with a graph Laplacian-based high-dimensional interpolating function which, in the continuum limit, converges to the solution of a Laplace–Beltrami equation on a high-dimensional manifold. Furthermore, we propose end-to-end training and testing algorithms for this new architecture. The proposed DNN with graph interpolating activation integrates the advantages of both deep learning and manifold learning. Compared to the conventional DNNs with the softmax function as output activation, the new framework demonstrates the following major advantages: First, it is better applicable to data-efficient learning in which we train high capacity DNNs without using a large number of training data. Second, it remarkably improves both natural accuracy on the clean images and robust accuracy on the adversarial images crafted by both white-box and black-box adversarial attacks. Third, it is a natural choice for semi-supervised learning. This paper is a significant extension of our earlier work published in NeurIPS, 2018. For reproducibility, the code is available at https://github.com/BaoWangMath/DNN-DataDependentActivation .

Mathematics↗

NASA NTRS2016

Tracking and Establishing Provenance of Earth Science Datasets: A NASA-based Example

Information quality is of paramount importance to science. Accurate, scientifically vetted and statistically meaningful and, ideally, reproducible information engenders scientific trust and research opportunities. Therefore, so-called Highly Influential Scientific Assessments (HISA) such as the U.S. Third National Climate Assessment undergo a very rigorous process to ensure transparency and credibility. As an activity to support the transparency of such reports, the U.S. Global Change Research Program has developed the Global Change Information System (GCIS). Specifically related to the transparency of NCA3, a recent activity was carried out to trace the provenance as completely as possible for all figures in the NCA3 report that predominantly used NASA data. This paper discusses lessons learned from this activity that trace the provenance of NASA figures in a major HISA-class pdf report.

Ramapriyan, Hampapuram K.↗

NASA NTRS1999

Project VISION (Very Intensive Scientific Intercurricular On-Site Education

Project VISION (Very Intensive Scientific Intercurricular On-Site Education) is a joint effort among NASA/John F. Kennedy Space Center, Florida International University, Universidad del Turabo, Miami-Dade County Public Schools and the Caguas/Gurabo Public Schools in Puerto Rico. The project's main mission is to institutionalize change among the elementary and middle school science and math teachers at participating schools so that their students receive continuously enriched instruction in the principles of science and math through the use of hands-on and minds-on experiments called learning modules. These leaming modules incorporate the national science and math education standards provided by the National Committee on Science Education Standards and Assessments and the National Council of Teachers of Mathematics, respectively. The use of learning modules that require hands-on and minds-on activities in a classroom setting garners great enthusiasm and motivation on the part of the target students for the understanding of the lesson's underlying math and science principles. With this enthusiasm and motivation, comes acceptance, attention, participation, discipline, acquiescence, and collaboration. Additionally, the use of hands-on activities may also require learning through a gamut of senses. Not only can the student use his/her eyes and ears during these activities, but most times, they can also use their senses of touch, smell, and taste, as well as intuition. Learning is, therefore, achieved using most or all the human senses. The combination of motivation/enthusiasm and the use of multiple senses creates an ideal environment conducive to leaming at a profound level.

Roig, Gustavo A.↗