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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Information Theoretic Extraction of EEG Features for Monitoring Subject Attention

The goal of this project was to test the applicability of information theoretic learning (feasibility study) to develop new brain computer interfaces (BCI). The difficulty to BCI comes from several aspects: (1) the effective data collection of signals related to cognition; (2) the preprocessing of these signals to extract the relevant information; (3) the pattern recognition methodology to detect reliably the signals related to cognitive states. We only addressed the two last aspects in this research. We started by evaluating an information theoretic measure of distance (Bhattacharyya distance) for BCI performance with good predictive results. We also compared several features to detect the presence of event related desynchronization (ERD) and synchronization (ERS), and concluded that at least for now the bandpass filtering is the best compromise between simplicity and performance. Finally, we implemented several classifiers for temporal - pattern recognition. We found out that the performance of temporal classifiers is superior to static classifiers but not by much. We conclude by stating that the future of BCI should be found in alternate approaches to sense, collect and process the signals created by populations of neurons. Towards this goal, cross-disciplinary teams of neuroscientists and engineers should be funded to approach BCIs from a much more principled view point.

Principe, Jose C.↗
NASA NTRSDate not supplied

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Rachel Dudukovich↗
DOE OSTI2021

Effects of 5-Ion Beam Irradiation and Hindlimb Unloading on Metabolic Pathways in Plasma and Brain of Behaviorally Tested WAG/Rij Rats

A limitation of simulated space radiation studies is that radiation exposure is not the only environmental challenge astronauts face during missions. Therefore, we characterized behavioral and cognitive performance of male WAG/Rij rats 3 months after sham-irradiation or total body irradiation with a simplified 5-ion mixed beam exposure in the absence or presence of simulated weightlessness using hindlimb unloading (HU) alone. Six months following behavioral and cognitive testing or 9 months following sham-irradiation or total body irradiation, plasma and brain tissues (hippocampus and cortex) were processed to determine whether the behavioral and cognitive effects were associated with long-term alterations in metabolic pathways in plasma and brain. Sham HU, but not irradiated HU, rats were impaired in spatial habituation learning. Rats irradiated with 1.5 Gy showed increased depressive-like behaviors. This was seen in the absence but not presence of HU. Thus, HU has differential effects in sham-irradiated and irradiated animals and specific behavioral measures are associated with plasma levels of distinct metabolites 6 months later. The combined effects of HU and radiation on metabolic pathways in plasma and brain illustrate the complex interaction of environmental stressors and highlights the importance of assessing these interactions.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRS2018

NASA Pilot-Engaged Expert Response Using IBM Watson Technology: Prototype Evaluation of Knowledge Retrieval System

NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.

Machine learning↗
NASA NTRS2021

Exploration Mission Tasks: A Technical Manual

This technical manual is an abridgement of the Generalizable Skills and Knowledge for Exploration Missions (NASA/CR-2018-22045) report (Stuster et al., 2019), describing research conducted under Cooperative Agreement 80NSSC18K0042 for the Human Factors and Behavioral Performance Element, Human Research Program, located at the National Aeronautics and Space Administration’s (NASA) Johnson Space Center. The research identified tasks that will be conducted by human crew during an expedition to Mars, and the abilities, skills, and knowledge that will be required of crew members. The 3-year study uses research methods that were developed to analyze the work performed by a variety of civilian and military occupational specialties and is consistent with Human Factors methods. The work began by developing a comprehensive inventory of 1,125 tasks that are likely to be performed during the 12 phases of the first human expeditions to Mars, from launch to landing 30 months later. Sixty subject matter experts (SMEs) rated expedition tasks in terms of (likely) frequency of performance, difficulty to learn, and importance to mission success; a fourth metric (criticality), was derived by summing the mean ratings of the three dimensions. Seventy-two SMEs placed the physical, cognitive, and social abilities necessary to perform the tasks in order of importance for specialist domains identified by the task analysis. The research team then identified: 1) Abilities, skills, and knowledge that can be retained and generalized across tasks and 2) Implications for crew size and composition. Study results also led to recommendations concerning equipment, habitats, and procedures for exploration-class space missions. Note: The full-mission task inventory was developed during a comprehensive review of documentation and concepts of operations. It was understood by the study team that the tasks were based on currently available information, and that the tools, equipment, propulsion methods, and/or other aspects of actual human expeditions to Mars might be different from those described here, as a consequence of technological development and evolving Mars Design Reference Missions. The purpose and scope of this technical manual is to present the core, actionable information that resulted from this research. The abridged format is intended to address the needs of development and research teams to quickly access, discern, and use the information in the course of their exploration-related work.

Brandin Munson↗
NASA NTRS2011

Cognitive Behavioral Training and Education for Spaceflight Operations

Cognitive behavioral-training (CBT) is an evidence-based practice commonly used to help treat insomnia, and is part of NASA's countermeasure regimen for Fatigue Management. CBT addresses the life style and habits of individuals that are maladaptive to managing stress and fatigue. This includes addressing learned behavioral responses that may cause stress and lead to an increased sense of fatigue. While the initial cause of onset of fatigue in the individual may be no longer present, the perception and engrained anticipation of fatigue persist and cause an exaggerated state of tension. CBT combined with relaxation training allows the individual to unlearn the maladaptive beliefs and behaviors and replace them with routines and techniques that allow cognitive restructuring and resultant relief from stress. CBT allows for elimination in individuals of unwanted ruminating thoughts and anticipatory anxiety by, for example, training the individuals to practice stressful situations in a relaxed state. As a result of CBT, relaxation can be accomplished in many ways, such as progressive muscle relaxation, meditation and guided imagery. CBT is not therapy, but rather the synthesis of behavioral countermeasures. CBT utilizes progressive relaxation as a means of reinforcing educational and cognitive countermeasures. These countermeasures include: masking, elimination of distracting thoughts, anxiety control, split attention, cognitive restructuring and other advanced psychological techniques.

Moonmaw, Ronald↗
NASA NTRS1988

An intelligent multi-media human-computer dialogue system

Sophisticated computer systems are being developed to assist in the human decision-making process for very complex tasks performed under stressful conditions. The human-computer interface is a critical factor in these systems. The human-computer interface should be simple and natural to use, require a minimal learning period, assist the user in accomplishing his task(s) with a minimum of distraction, present output in a form that best conveys information to the user, and reduce cognitive load for the user. In pursuit of this ideal, the Intelligent Multi-Media Interfaces project is devoted to the development of interface technology that integrates speech, natural language text, graphics, and pointing gestures for human-computer dialogues. The objective of the project is to develop interface technology that uses the media/modalities intelligently in a flexible, context-sensitive, and highly integrated manner modelled after the manner in which humans converse in simultaneous coordinated multiple modalities. As part of the project, a knowledge-based interface system, called CUBRICON (CUBRC Intelligent CONversationalist) is being developed as a research prototype. The application domain being used to drive the research is that of military tactical air control.

Neal, J. G.↗
NASA NTRS2013

Human Factors in Accidents Involving Remotely Piloted Aircraft

This presentation examines human factors that contribute to RPA mishaps and provides analysis of lessons learned. RPA accident data from U.S. military and government agencies were reviewed and analyzed to identify human factors issues. Common contributors to RPA mishaps fell into several major categories: cognitive factors (pilot workload), physiological factors (fatigue and stress), environmental factors (situational awareness), staffing factors (training and crew coordination), and design factors (human machine interface).

Merlin, Peter William↗
NASA NTRS2016

Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders: Evidence Report

In April 2010, President Obama declared a space pioneering goal for the United States in general and NASA in particular. "Fifty years after the creation of NASA, our goal is no longer just a destination to reach. Our goal is the capacity for people to work and learn and operate and live safely beyond the Earth for extended periods of time, ultimately in ways that are more sustainable and even indefinite." Thus NASA's Strategic Objective 1.1 emerged as "expand human presence into the solar system and to the surface of Mars to advance exploration, science, innovation, benefits to humanity, and international collaboration" (NASA 2015b). Any space flight, be it of long or short duration, occurs in an extreme environment that has unique stressors. Even with excellent selection methods, the potential for behavioral problems among space flight crews remain a threat to mission success. Assessment of factors that are related to behavioral health can help minimize the chances of distress and, thus, reduce the likelihood of adverse cognitive or behavioral conditions and psychiatric disorders arising within a crew. Similarly, countermeasures that focus on prevention and treatment can mitigate the cognitive or behavioral conditions that, should they arise, would impact mission success. Given the general consensus that longer duration, isolation, and confined missions have a greater risk for behavioral health ensuring crew behavioral health over the long term is essential. Risk, which within the context of this report is assessed with respect to behavioral health and performance, is addressed to deter development of cognitive and behavioral degradations or psychiatric conditions in space flight and analog populations, and to monitor, detect, and treat early risk factors, predictors and other contributing factors. Based on space flight and analog evidence, the average incidence rate of an adverse behavioral health event occurring during a space mission is relatively low for the current conditions. While mood and anxiety disturbances have occurred, no behavioral emergencies have been reported to date in space flight. Anecdotal and empirical evidence indicate that the likelihood of an adverse cognitive or behavioral condition or psychiatric disorder occurring greatly increases with the length of a mission. Further, while cognitive, behavioral, or psychiatric conditions might not immediately and directly threaten mission success, such conditions can, and do, adversely impact individual and crew health, welfare, and performance.

Slack, Kelley J.↗
DOE OSTI2021

Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking

"Detect the expected, discover the unexpected" was the founding principle of the field of visual analytics. This mantra implies that human stakeholders, like a domain expert or data analyst, could leverage visual analytics techniques to seek answers to known unknowns and discover unknown unknowns in the course of the data sensemaking process. We argue that in the era of AI-driven automation, we need to recalibrate the roles of humans and machines (e.g., a machine learning model) as teammates. We posit that by realizing human-machine teams as a stakeholder unit, we can better achieve the best of both worlds: automation transparency and human reasoning efficacy. However, this also increases the burden on analysts and domain experts towards performing more cognitively demanding tasks than what they are used to. In this paper, we reflect on the complementary roles in a human-machine team through the lens of cognitive psychology and map them to existing and emerging research in the visual analytics community. We discuss open questions and challenges around the nature of human agency and analyze the shared responsibilities in human-machine teams.

Sensemaking, human-machine teaming, agency, artifi↗
NASA NTRSDate not supplied

Habitability Assessments And Lessons-learned From 3-day And 11-day Enriched Oxygen Hypobaric Chamber Tests At NASA Johnson Space Center

INTRODUCTION: Decompression sickness (DCS) is a risk to the health and performance of astronauts and high-altitude aircrew. Tolerance to flammability, hypoxia, prebreathe duration, and DCS risk varies across different organizations, vehicles, suits, and destinations, necessitating a variety of DCS risk mitigation approaches. Existing models of altitude DCS risk are often insufficient to enable accurate risk-informed decisions during hardware development, mission planning, and flight operations. METHODS: NASA completed outfitting of a dedicated facility at Johnson Space Center to support testing of up to eight human subjects for multiple days in hypobaric and enriched oxygen atmospheres. The primary purpose of the testing capability is validation of DCS risk mitigation protocols for Artemis missions to the Moon; however, it will also support development and validation of a generalizable altitude DCS risk estimation tool. A 3-day and an 11-day prebreathe validation test were completed in 2022, each with 8 human subjects living at 56.5 kPa (8.2 psia), 34% O2, 66% N2, with 5 simulated EVAs performed on masks at 29.6 kPa (4.3 psi), 85% O2, 15% N2. Facility and organizational lessons-learned and process improvements were recorded during and following the tests, and subjective habitability ratings were recorded daily during the 11-day test. Hypoxia and DCS-related physiological and cognitive outcome measures were recorded during both tests and are reported in companion presentations. RESULTS & DISCUSSION: All subjects completed each of the tests. Primary habitability issues related to mask discomfort during simulated EVAs and poor sleep quality due to thin mattresses. Polybenzimidazole (PBI) clothing was worn by all subjects due to the increased fire risk and may be required for Artemis missions; clothing was found to be acceptable overall with the worst ratings being due to poor fit and inelasticity. Chamber O2 and CO2 sensor inconsistency was observed that did not result in test termination but required post-test follow-up. Forward plans include additional hypobaric testing and integration of existing and future physiological outcome data into an open-source Aerospace Estimation Tool for Hypobaric Exposure Risk (AETHER). NASA is also working to make the testing capability available to commercial companies.

Andrew F J Abercromby↗
NASA NTRSDate not supplied

Habitability Assessments And Lessons-learned From 3-day And 11-day Enriched Oxygen Hypobaric Chamber Tests At NASA Johnson Space Center

INTRODUCTION: Decompression sickness (DCS) is a risk to the health and performance of astronauts and high-altitude aircrew. Tolerance to flammability, hypoxia, prebreathe duration, and DCS risk varies across different organizations, vehicles, suits, and destinations, necessitating a variety of DCS risk mitigation approaches. Existing models of altitude DCS risk are often insufficient to enable accurate risk-informed decisions during hardware development, mission planning, and flight operations. METHODS: NASA completed outfitting of a dedicated facility at Johnson Space Center to support testing of up to eight human subjects for multiple days in hypobaric and enriched oxygen atmospheres. The primary purpose of the testing capability is validation of DCS risk mitigation protocols for Artemis missions to the Moon; however, it will also support development and validation of a generalizable altitude DCS risk estimation tool. A 3-day and an 11-day prebreathe validation test were completed in 2022, each with 8 human subjects living at 56.5 kPa (8.2 psia), 34% O2, 66% N2, with 5 simulated EVAs performed on masks at 29.6 kPa (4.3 psi), 85% O2, 15% N2. Facility and organizational lessons-learned and process improvements were recorded during and following the tests, and subjective habitability ratings were recorded daily during the 11-day test. Hypoxia and DCS-related physiological and cognitive outcome measures were recorded during both tests and are reported in companion presentations. RESULTS & DISCUSSION: All subjects completed each of the tests. Primary habitability issues related to mask discomfort during simulated EVAs and poor sleep quality due to thin mattresses. Polybenzimidazole (PBI) clothing was worn by all subjects due to the increased fire risk and may be required for Artemis missions; clothing was found to be acceptable overall with the worst ratings being due to poor fit and inelasticity. Chamber O2 and CO2 sensor inconsistency was observed that did not result in test termination but required post-test follow-up. Forward plans include additional hypobaric testing and integration of existing and future physiological outcome data into an open-source Aerospace Estimation Tool for Hypobaric Exposure Risk (AETHER). NASA is also working to make the testing capability available to commercial companies.

Andrew Abercromby↗
NASA NTRS2024

Physiological and Subjective Responses of Pilots during Advanced Air Mobility Flight Testing with Automated Systems

Aviation is constantly evolving, mostly due to the integration of automated systems into the National Airspace System. This presents a host of challenges and opportunities. NASA’s Advanced Air Mobility (AAM) project has taken a significant leap forward with a research flight test led by the Integration of Automated Systems (IAS) sub-project. This effort focused on assessing automated flight deck algorithms essential for supporting high-density Urban Air Mobility (UAM) operations. Carrying out a two-ship flight test in UAM Maturity Level 4 scenarios, the team evaluated state-of-the-art algorithms, including Hazard Perception and Avoidance (HPA) and flight path management (FPM) systems. This paper investigates into pilots’ physiological and subjective responses during the flight scenarios conducted. In collaboration with Lockheed Martin Advanced Technology Laboratories (ATL), we collected and analyzed live-flight biometric data using eye tracking, mobile brain imaging, and heart rate sensors. The study provides insights into pilots’ workload and cognitive engagement while navigating automated tools, leveraging both biometric data and post-encounter subjective assessments. The research highlights the interaction between human operators and automated systems, contributing valuable lessons learned about gathering human data in live-flight environments. The knowledge acquired from this study enhances our understanding of human factors in automated flight and informs future studies attempting to undertake similar feats.

Kevin J. Monk↗
NASA NTRSDate not supplied

Physiological and Subjective Responses of Pilots During Advanced Air Mobility Flight Testing With Automated Systems

Aviation is constantly evolving, mostly due to the integration of automated systems into the National Airspace System. This presents a host of challenges and opportunities. NASA's Advanced Air Mobility (AAM) project has taken a significant leap forward with a research flight test led by the Integration of Automated Systems (IAS) sub-project. This effort focused on assessing automated flight deck algorithms essential for supporting high-density Urban Air Mobility (UAM) operations. Carrying out a two-ship flight test in UAM Maturity Level 4 scenarios, the team evaluated state-of-the-art algorithms, including Hazard Perception and Avoidance (HPA) and flight path management (FPM) systems. This presentation focuses on results of pilots' physiological and subjective responses during the flight scenarios conducted. In collaboration with Lockheed Martin Advanced Technology Laboratories (ATL), we collected and analyzed live-flight biometric data using eye tracking, mobile brain imaging, and heart rate sensors. The data provides insights into pilots' workload and cognitive engagement while navigating automated tools, leveraging both biometric data and post-encounter subjective assessments. The research highlights the interaction between human operators and automated systems, contributing valuable lessons learned about gathering human data in live-flight environments. The knowledge acquired from this endeavor enhances our understanding of human factors in automated flight and informs future studies attempting to undertake similar feats.

air mobility↗
NASA NTRS2020

Enabling Dynamic Vehicle Analyses With Improved Atmospheric Attenuation Models in Glenn Research Center Communication Analysis Suite

To aid in meeting the NASA objective of returning humans to the Moon, the Glenn Research Center’s Communication Analysis Suite was augmented with two distinct capabilities. The first capability added was the vehicle propagator. This allows the addition of dynamic aircraft and ground vehicles around any celestial body within the solar system during an analysis. This functionality interpolates the position and velocity of the vehicle relative to a celestial body at the time steps analyzed using the type of path and either a series of waypoints or a direction and duration of travel. The implications of this new capability include lunar rovers and/or drones, such as Dragonfly, where the vehicle propagator will analyze the communications architecture. The newly created vehicle propagator is now in use in communications studies for the 2024 lunar missions, simulating the movement of lunar rovers across the Moon’s southern pole. The second capability added was the augmentation of the atmospheric attenuation model. The previous model did not have a uniform low-elevation attenuation model due to the trigonometric approximation for path length and the exponential nature of low-elevation scintillation. User-defined weather parameters were also added to the updated atmospheric attenuation model. The previous model solely used tabular data based upon the season and location of the transmitting antenna. Multiple simulations of the same configuration now return different results based on the differing weather parameters. Cognitive communications analysis efforts can use this second capability to generate neural network training data based on differing weather conditions at utilized ground stations, a critical step in allowing neural networks to learn how weather parameters impact communications performance.

vehicle propagation↗
DOE OSTI2025

The Impact of Cultural Values and Organizational Processes on Nuclear Security Operations

Human performance is a pivotal factor in the design, testing, maintenance, and operation of security systems. The effectiveness of these systems relies not only on the capabilities, limitations, motives, and attitudes of the individuals involved, but also on the quality of training, instructional content, and evaluation methods provided. To uphold security standards, seamless integration between technologies and operators necessitates reliable human input. In security operations, human errors, often attributed to blame, sanctions, low motivation, individual accountability, or complacency, are primary causes of system failures. Complacency, characterized by a false sense of security, reflects a lack of awareness of potential threats and is a significant contributing factor to lapses in security. Security incidents arise from various factors, many extend beyond individual control, highlighting the need for a holistic approach to human performance that integrates organizational processes and team collaboration. Historically, errors have been attributed to individual moral or cognitive failures. However, insights from Operational Experiences (OEs) suggest that organizational processes weakness and deficiencies in nuclear cultural values contribute more significantly to security failures than individual mistakes. This paper consolidates lessons learned from diverse international nuclear security cultures and aims to highlight the importance of security culture in shaping global perspectives on nuclear security. It underscores the role of cultural values in shaping nuclear security practices and enhancing the resilience of security systems in the nuclear sector.

Zineddin, Dr. Z. [ORNL] (ORCID:0009000848740725)↗
NASA NTRSDate not supplied

CLAIRE: Enabling Heterogeneous Communication Network Optimization for Robust and Resilient Operations

In this paper, we present the capabilities of the CLAIRE System which provides resilient communications for NASA in presence of interference and congestion for a heterogeneous multi-vendor network. CLAIRE increases mission science data return to improve resource efficiencies and ensures resilience in the unpredictable space environment for NASA missions and communication networks. CLAIRE provides technology / waveform agnostic cognitive control plane that is instantiated at the Application Layer (APP) so that it can ride on NASA’s HDTN bundle protocol or any other protocol stack that is used by the network. The cognitive control plane is instantiated using Heartbeats (HTBTs). CLAIRE is assisted by Wideband UHF-Ka Band RF Sensing that leverages advances in the Direct Digital Transceiver (DDTRX) technology. The Wideband RF Sensing is driven by statistical signal processing and machine learning algorithms. Interference is mitigated using Dynamic Spectrum Access (DSA). Finally, CLAIRE addresses congestion using spectrum aware packet forwarding algorithm. CLAIRE provides an extensible protocol that allows passing of RF spectrum situational awareness, cross-layer sensing, delay tolerant networking and dynamic spectrum access information that can help with network optimization. Cross-Layer Sensing (CLS) and CLAIRE Decision Engine (CDE) enable spectrum and delay aware packet forwarding and Dynamic Spectrum Access during cases of severe interference.

cognitive communications↗
DOE OSTI2021

Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.

60 APPLIED LIFE SCIENCES↗