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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Integrated Cyber/Physical Grid Resiliency Modeling

This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Nuclear Thermal Propulsion Ground Test History

Nuclear Thermal Propulsion (NTP) was started in ~1955 under the Atomic Energy Commission as project Rover and was assigned to Los Alamos National Laboratory. The Nevada Test Site was selected in 1956 and facility construction began in 1957. The KIWI-A was tested on July 1, 1959 for 5 minutes at 70MW. KIWI-A1 was tested on July 8, 1960 for 6 minutes at 85MW. KIWI-A3 was tested on October 10, 1960 for 5 minutes at 100MW. The National Aeronautics and Space Administration (NASA) was formed in 1958. On August 31, 1960 the AEC and NASA established the Space Nuclear Propulsion Office and named Harold Finger as Director. Immediately following the formation of SNPO, contracts were awarded for the Reactor In Flight Test (RIFT), master plan for the Nuclear Rocket Engine Development Station (NRDS), and the Nuclear Engine for Rocket Vehicle Application (NERVA). From December 7, 1961 to November 30, 1962, the KIWI-B1A, KIWI-B1B, and KIWI-B4A were tested at test cell A. The last two engines were only tested for several seconds before noticeable failure of the fuel elements. Harold Finger called a stop to any further hot fire testing until the problem was well understood. The KIWI-B4A cold flow test showed the problem to be related to fluid dynamics of hydrogen interstitial flow causing fuel element vibrations. President Kennedy visited the NTS one week after the KIWI-B4A failure and got to see the engine starting to be disassembled in the maintenance facility. The KIWI-B4D and KIWI-B4E were modified to not have the vibration problems and were tested in test cell C. The NERVA NRX program started testing in early 1964 with NRX-A1 cold flow test series (unfueled graphite core), NRX-A2 and NRX-A3 power test series up to 1122 MW for 13 minutes. In March 1966, the NRX-EST (Engine System Test) was the first breadboard using flight functional relationship and total operating time of 116 minutes. The NRX-EST demonstrated the feasibility of a hot bleed cycle. The NRX-A5 had multiple start-ups in May-June 1966 with 30.75 minutes accumulative operating time at or above 1GW. The NRX-A6 was tested in December 1969 and ran for 62 minutes at 1100 MW. Each engine had post-test examination and found various structure anomalies which were identified for correction and the fuel element corrosion rate was reduced. The Phoebus series of research reactors began testing at test cell C, in June 1965 with Phoebus 1A. Phoebus 1A operated for 10.5 minutes at 1100 MW before unexpected loss of propellant and leading to an engine breakdown. Phoebus 1B ran for 30 minutes in February of 1967. Phoebus 2A was the highest steady state reactor built at 5GW. Phoebus 2A ran for 12 minutes at 4100 MW demonstrating sufficient power is available. The Peewee test bed reactor was tested November- December 1968 in test cell C for 40 minutes at 500MW with overall performance close to pre-run predictions. The XE' engine was the only engine tested with close to a flight configuration and fired downward into a diffuser at the Engine Test Stand (ETS) in 1969. The XE' was 1100 MW and had ~28 start-ups. The nuclear furnace NF-1 was operated at 44 MW with multiple test runs at 90 minutes in the summer of 1972. The NF-1 was the last NTP reactor tested. The Rover/NERVA program was cancelled in 1973. However, before cancellation, a lot of other engineering work was conducted by Aerojet on a 75, 000 lbf prototype flight engine and by Los Alamos on a ~16,000 lbf "Small Engine" nuclear rocket design. The ground test history of NTP at the NRDS also offers many lessons learned on how best to setup, operate, emergency shutdown, and post-test examine NTP engines. The reactor and engine maintenance and disassembly facilities were used for assembly and inspection of radioactive engines after testing. Most reactor/ engines were run at test cell A or test cell C with open air exhaust. The Rover/NERVA program became aware of a new environmental regulation that would restrict the amount of radioactive particulates allowed to be release in open air and successfully demonstrated a scrubber concept with the NF-1. The ETS stand was the only one with a high altitude test chamber used for XE'. The ETS and other test cells showed the effects the engine's radiation had on the facility materials and instrumentation as well as side effects the ground test facility has back on the engine operation. The breakdown of Phoebus 1A at test cell C showed how the site was cleaned up and back to operation for five more engines before the program was cancelled.

Gerrish, Harold P.↗
NASA NTRS2014

The OSIRIS-Rex Asteroid Sample Return: Mission Operations Design

The OSIRIS-REx mission employs a methodical, phased approach to ensure success in meeting the missions science requirements. OSIRIS-REx launches in September 2016, with a backup launch period occurring one year later. Sampling occurs in 2019. The departure burn from Bennu occurs in March 2021. On September 24, 2023, the SRC lands at the Utah Test and Training Range (UTTR). Stardust heritage procedures are followed to transport the SRC to Johnson Space Center, where the samples are removed and delivered to the OSIRIS-REx curation facility. After a six-month preliminary examination period the mission will produce a catalog of the returned sample, allowing the worldwide community to request samples for detailed analysis.Traveling and returning a sample from an Asteroid that has not been explored before requires unique operations consideration. The Design Reference Mission (DRM) ties together space craft, instrument and operations scenarios. The project implemented lessons learned from other small body missions: APLNEAR, JPLDAWN and ESARosetta. The key lesson learned was expected the unexpected and implement planning tools early in the lifecycle. In preparation to PDR, the project changed the asteroid arrival date, to arrive one year earlier and provided additional time margin. STK is used for Mission Design and STKScheduler for instrument coverage analysis.

0↗
DOE OSTI2025

Quantum optimal control of superconducting qubits based on machine-learning characterization

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations in detailed numerical simulations of a superconducting transmon qubit. Furthermore, we demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of ∼99.99%.

Artificial neural networks↗
DOE OSTI2021

Predictive Data Analytics Framework Using Advanced Test Reactor Acoustic Data

Although a nuclear reactor is a hostile environment for sensing and electrical communications, the reactor core is amenable to acoustic communication. An acoustic measurement infrastructure (AMI) has been installed at the Advanced Test Reactor (ATR) nozzle trench area to record acoustic signals that has the ability to capture different operating regime of the reactor. This AMI includes ATR in-pile structural components, coolant, acoustic receivers, primary coolant pumps (PCP) as signal sources, a data acquisition system, and signal-processing algorithms, enabling real-time. This report will discusses development of recursive Fast Fourier Transform approach to process in real-time acoustic signals, application of short time Fast Fourier Transform to the ATR brush data to understand the vibration level and to develop spectrograms for different primary coolant pump combinations. The combination of primary coolant pumps for normal and power axial locator mechanism of ATR are different and generates different signatures. These acoustic signatures were used to develop machine learning approaches to automatically classify different operating regimes. This lay the foundation for predictive analytic framework that can be leverage by ATR to optimize their operation and maintenance. The path forward involves continued engagement with ATR and expanded implementation of AMI and predictive framework at ATR and other facilities within INL and at other experimental reactors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Learnings From the 2022 NREL Partner Forum: Research Needs for Airport Ground Infrastructure and Operations

During the 2022 Partner Forum at the National Renewable Energy Laboratory (NREL), stakeholders and experts from across aviation came to the whiteboard to outline common goals, align interests, understand technologies, and discuss steps for safely and seamlessly decarbonizing aviation. Attendees identified challenges, strategies, and needed research on aviation fuels, infrastructure, and aircraft. Initial insights, strategies, and high-level suggestions from an airport ground infrastructure and operations workshop are summarized below, including potential barriers and synergies for achieving bold decarbonization goals.

airport↗
NASA NTRS2004

Scripting Scenarios for the Human Patient Simulator

The Human Patient Simulator (HPS) is particularly useful in providing scenario-based learning which can be tailored to fit specific scenarios and which can be modified in realtime to enhance the teaching environment. Scripting these scenarios so as to maximize learning requires certain skills, in order to ensure that a change in student performance, understanding, critical thinking, and/or communication skills results. Methods: A "good" scenario can be defined in terms of applicability, learning opportunities, student interest, and clearly associated metrics. Obstacles to such a scenario include a lack of understanding of the applicable environment by the scenario author(s), a desire (common among novices) to cover too many topics, failure to define learning objectives, mutually exclusive or confusing learning objectives, unskilled instructors, poor preparation , disorganized approach, or an inappropriate teaching philosophy (such as "trial by fire" or education through humiliation). Results: Descriptions of several successful teaching programs, used in the military, civilian, and NASA medical environments , will be provided, along with sample scenarios. Discussion: Simulator-based lessons have proven to be a time- and cost-efficient manner by which to educate medical personnel. Particularly when training for medical care in austere environments (pre-hospital, aeromedical transport, International Space Station, military operations), the HPS can enhance the learning experience.

Bacal, Kira↗
DOE OSTI2023

Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60- Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states but also individual load operating power levels. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy, good scaling and generalization properties.

feature extraction↗
NASA NTRS1987

Launch Processing System operations with a future look to Operations Analyst (OPERA)

The Launch Processing System architecture and the ground support operations required to provide Shuttle System engineers with the capability to safely process and launch an Orbiter are described. The described ground operations are the culmination of eleven years of experience and redesign. Some of the 'lessons learned' are examined, and problem areas which ground support operations have identified over the years as the Shuttle and Launch Processing Systems continue to grow in complexity are discussed. The Operational Analyst for Distributed Systems (OPERA), a proposed set of expert systems for the Launch Processing System Operational assistance, is discussed along with its extensions to prospective future configurations and components for the Launch Processing System.

Heard, Astrid E.↗
NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗
NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗
NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗
DOE OSTI2025

Explainable and Differentiable Reinforcement Learning for Multi-objective Optimization in Particle Accelerators

Operating particle accelerators involves optimizing multiple goals simultaneously, which can be challenging due to trade-offs among objectives. While evolutionary algorithms like the genetic algorithm (GA) have been used for various Multi-Objective Optimization (MOO) tasks, they are not inherently suited for complex control problems. This talk highlights two variations of Reinforcement Learning (RL) for concurrently optimizing heat load and trip rates at the Continuous Electron Beam Accelerator Facility (CEBAF). The problem involves strict constraints on individual states, actions, and overall energy requirements of the beam. First, this talk highlights how differentiability can be harnessed through a Deep Differentiable Reinforcement Learning (DDRL) approach to address MOO issues within particle accelerators. We examine the DDRL method alongside Model Free Reinforcement Learning (MFRL), GA, and Bayesian Optimization (BO). The performance of these methods is assessed by generating a Pareto-front for two objectives. Our findings indicate that DDRL excels in handling high-dimensional problems more effectively than MFRL, BO, and GA. Next, we will show integration of explainable physics-based constraints into RL algorithms to enhance trans- parency and trust in decision-making processes by enabling users to verify that agents adhere to established physical principles. This surrogate function can be modeled using neural networks or sparse dictionary mod- els. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment provided but the surrogate model. In addi- tion, we find that the introduction of a mathematical functional dictionary based surrogate model enables our reinforcement learning algorithms to reliably converge for difficult high-dimensional accelerator controls environments.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2019

Underground Natural Gas Storage – Analog Studies to Geologic Storage of CO 2

This study focuses on underground natural gas storage operations and CO 2 geologic storage in saline-bearing formations; both individually and in relation to each other. This study is the first of three that evaluate analog industries of CO 2 storage (second focuses on deep well waste disposal using U.S. EPA UIC Class I wells and third on CO 2 EOR). Several pieces of information can be found throughout the study including overview of underground natural gas storage history and side-by-side comparisons of major synergistic features between underground natural gas storage and CO 2 storage in saline-bearing formations. Best practices and lessons learned from underground natural gas storage operations can provide perspective from which future CO 2 storage R&D pursuits and field projects can benefit. A summary document providing a high-level overview and key takeaways of this study is available on NETL’s website under the Collection Name: NETL’s Analog Studies to Geologic Storage of CO 2 .

03 NATURAL GAS↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
NASA NTRS1997

Space Mechanisms Lessons Learned and Accelerated Testing Studies

A number of mechanism (mechanical moving component) failures and anomalies have recently occurred on satellites. In addition, more demanding operating and life requirements have caused mechanism failures or anomalies to occur even before some satellites were launched (e.g., during the qualification testing of GOES-NEXT, CERES, and the Space Station Freedom Beta Joint Gimbal). For these reasons, it is imperative to determine which mechanisms worked in the past and which have failed so that the best selection of mechanically moving components can be made for future satellites. It is also important to know where the problem areas are so that timely decisions can be made on the initiation of research to develop future needed technology. To chronicle the life and performance characteristics of mechanisms operating in a space environment, a Space Mechanisms Lessons Learned Study was conducted. The work was conducted by the NASA Lewis Research Center and by Mechanical Technologies Inc. (MTI) under contract NAS3-27086. The expectation of the study was to capture and retrieve information relating to the life and performance of mechanisms operating in the space environment to determine what components had operated successfully and what components had produced anomalies.

Fusaro, Robert L.↗
DOE OSTI2021

Bilinear dynamic mode decomposition for quantum control

Abstract Data-driven methods for establishing quantum optimal control (QOC) using time-dependent control pulses tailored to specific quantum dynamical systems and desired control objectives are critical for many emerging quantum technologies. We develop a data-driven regression procedure, bilinear dynamic mode decomposition (biDMD), that leverages time-series measurements to establish quantum system identification for QOC. The biDMD optimization framework is a physics-informed regression that makes use of the known underlying Hamiltonian structure. Further, the biDMD can be modified to model both fast and slow sampling of control signals, the latter by way of stroboscopic sampling strategies. The biDMD method provides a flexible, interpretable, and adaptive regression framework for real-time, online implementation in quantum systems. Further, the method has strong theoretical connections to Koopman theory, which approximates nonlinear dynamics with linear operators. In comparison with many machine learning paradigms minimal data is needed to construct a biDMD model, and the model is easily updated as new data is collected. We demonstrate the efficacy and performance of the approach on a number of representative quantum systems, showing that it also matches experimental results.

97 MATHEMATICS AND COMPUTING↗