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NASA NTRS2019

Physically-Informed Artificial Neural Networks for Atomistic Modeling of Materials

A new approach is presented for the development of classical interatomic potentials using physically-informed neural networks (PINN) combined with an analytical bond-order atomic interaction model. Due to the strong physical underpinnings, the PINN potentials demonstrate much better transferability than the existing machine-learning potentials while drastically improving the accuracy in comparison with traditional potentials. PINN potentials can be constructed for both metallic and covalent materials in a unified manner. A number of applications of PINN potentials to large-scale molecular dynamics and Monte Carlo simulations and calculation of thermal and mechanical properties of diverse materials are demonstrated. Some of the specific materials systems include silicon and aluminum, as well as alloys and compounds. Computational aspects of PINN potentials are discussed and future developments in this field are outlined.

J Hickman↗

NASA NTRS2022

pyCRTM: A Python Interface for the Community Radiative Transfer Model

The Community Radiative Transfer Model (CRTM) is a powerful and versatile scalar radiative transfer model for satellite data assimilation and remote sensing applications. It is implemented as an object-oriented Fortran library, enabling flexible code development and optimal runtime performance on clusters. The downsides of the Fortran interface are a steep learning curve for students and the reduced productivity of users that is typical for static compiled languages, in contrast to dynamic interpreted languages like Python. pyCRTM is a new software framework that directly interfaces the CRTM Fortran data structures and procedures in Python, leveraging both the simplicity and ease of use of Python syntax as well as the flexibility arising from the vast contemporary Python ecosystem. The goal of pyCRTM is to lower the barrier of entry for university students to learn and use the CRTM and to boost the productivity of researchers seeking to create new methods in radiative transfer and data assimilation, or seeking to apply the CRTM to study atmospheric phenomena without having to go through the pre-existing complexity of the CRTM Fortran interface.

Python↗

NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗

NASA NTRSDate not supplied

Modal Test and Model Correlation of NASA Plum Brook Station Mechanical Vibration Facility Head Expander –Lessons Learned from the Perspective of an Early-Career Engineer

In preparation for the Sierra Nevada Corporation’s (SNC) Dream Chaser spacecraft vibration test campaign at the Mechanical Vibration Facility (MVF) at NASA Plum Brook Station (PBS) in Sandusky, Ohio, a test-verified model of MVF is needed in order to be able to perform accurate pretest analysis used for determining response limits and abort levels. MVF was designed to vibration test MPCV Orion and was used to perform the system level vibration test of the European Service Module Structural Test Article (E-STA) in 2016. MVF is comprised of an 18 ft diameter annulus table that is driven with sixteen hydraulic vertical actuator assemblies and four hydraulic horizontal actuator assemblies, which allow it to perform single axis vibration testing in the vertical axis and in each of the two orthogonal horizontal axes without the need for reconfiguring the test article. A head expander for the MVF Table has been designed and built that fills in the center opening providing a continuous flat mounting surface with a maximum diameter of 16.25feet that expands the vibration testing capabilities of MVF. The MVF Table with this head expander will be used during the SNC Dream Chaser spacecraft vibration test campaign. Therefore, a critical element in a test-verified model of the MVF will be a test correlated finite element model (FEM) of the head expander. To obtain this, engineers from the Structural Dynamics Lab (SDL) at NASA Glenn Research Center (GRC) in Cleveland, Ohio performed a modal pretest analysis, conducted a modal test in July 2019, and most recently correlated the head expander finite element model to the modal test data up to 300 Hz. From the initial test preparations to the final delivery of a correlated finite element model, all efforts mentioned were led by the same early-career engineers at NASA GRC. From the viewpoint of an early-career engineer, lessons learned about modal pretest analysis, modal testing, and finite element model correlation of the MVF Table expander head will be presented and discussed. This will include the importance of understanding the limitations of using uncorrelated finite element models in the modal pretest analysis and planning, the importance of orthogonality metrics in judging adequacy and accuracy of test mode shapes, and the importance of having the FEM match the as built hardware in the model correlation effort.

Emma L Pierson↗

NASA NTRS2013

Lessons Learned from the Stardust Sample Return Mission

These are science and mission design and operations lessons learned from the Stardust Mission, which returned grains from comet Wild-2 and fresh interstellar dust to Earth in 2006 [1]. Science Lessons: Major discoveries of the Stardust Wild-2 samples include the presence of numerous chondrules and CAI in a comet, which requires a much more dynamic early solar system than many had envisaged, and verified predictions made by models requiring outward flow of early solar system solids before the early nebular gas had dissipated [1-3]. No evidence has been found for the presence of live Al-26 in the comet, suggesting late accretion [4]. Carbonates and unusual sulfides were found which potentially require activity of liquid water within the comet, but to only a minor degree at best [5-6]. The presence of abundant thermally-metamorphosed silicates in Wild-2 appears to require assembly from an earlier generation of bodies [7]. The abundance of presolar grains in the Wild-2 samples appears to be below what has been found in most chondritic IDPs and primitive chondrites [1]. The bulk mineralogy of Wild-2 grains does not match the mineralogy from any single other known astromaterial [7], and is also strikingly unlike that inferred from Spitzer Telescope spectra of Comet Temple 1 dust [8]. Amino acids and other fragile organics have been detected among the Wild-2 samples [9], which highlights the critical importance of further developing techniques for organic analysis in small samples, and cleaning outbound spacecraft.

Zolensky, M. E.↗

NASA NTRS1999

A Survey of Collective Intelligence

This chapter presents the science of "COllective INtelligence" (COIN). A COIN is a large multi-agent systems where: i) the agents each run reinforcement learning (RL) algorithms; ii) there is little to no centralized communication or control; iii) there is a provided world utility function that, rates the possible histories of tile full system. Tile conventional approach to designing large distributed systems to optimize a world utility does not use agents running RL algorithms. Rather that approach begins with explicit modeling of the overall system's dynamics, followed by detailed hand-tuning of the interactions between the components to ensure that they "cooperate" as far as the world utility is concerned. This approach is labor-intensive, often results in highly non-robust systems, and usually results in design techniques that, have limited applicability. In contrast, with COINs we wish to solve the system design problems implicitly, via the 'adaptive' character of the RL algorithms of each of the agents. This COIN approach introduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we do not have phenomena like the tragedy of the commons, or Braess's paradox? Although still very young, the science of COINs has already resulted in successes in artificial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur's "El Farol bar problem". It is expected that as it matures not only will COIN science expand greatly the range of tasks addressable by human engineers, but it will also provide much insight into already established scientific fields, such as economics, game theory, or population biology.

Wolpert, David H.↗

NASA NTRS2025

Navigating Team Dynamics: Automated Detection of Micro-Behaviors Between Team Members Through Longitudinal Interaction Data

The success in future long term space exploration missions will depend on the cooperation, coordination, and mutual understanding among the crew members. Micro-behaviors are momentary, subtle linguistic and paralinguistic indicators of thinking and feeling toward another member of the team (Cortina et al., 2001; Smith & Griffiths, 2022) that can significantly impact team dynamics and influence the overall team performance (Paromita & Chaspari, 2024). Due to their interactive nature, micro-behaviors have a sender (i.e., the team member expressing the micro-behavior) and a target (the team member impacted by the micro-behavior). Detection of these behaviors can assist in avoiding possible conflict among crew members and promoting the overall team success. Our prior research focused on an initial proof of concept of machine learning (ML) models and natural language processing (NLP) techniques that were used for automatically detect micro-behaviors among crew members of the US National Aeronautics and Space Administration’s (NASA) Human Exploration Research Analog (HERA) Campaigns 4 and 5 missions (Paromita et al., 2023). Results underscored the importance of incorporating contextual information in the ML models in the form of sentiment analysis, type of task, and dyadic interaction among team members. Here, we expand the scope of our prior work in two ways. First, we assess ML/NLP methods on new behavioral annotations coded using an adapted version of Smith & Griffins (2022) theoretical framework in terms of Violation (i.e., presence of valenced behavior, uplifting/positive or discouraging/negative), Intensity (i.e., force of behavior in terms of how uplifting or discouraging is the behavior), and Intent (i.e., motive of the behavior in terms of whether it was deliberate or unintentional). Second, we expand the design of the ML model to preserve information about the role of each team member within the occurrence of the micro-behavior (in contrast to the previous model that only considered the sender and the target without determining the team member role). This allows to consider all team members' contributions in the conversation and model long-term dependencies in the dialogue. Our experiments for this study are conducted on data from 5 teams of the NASA HERA C4 (NASA grant NNX16AQ48G (PI: Bell)). Conversations were extracted from the 1.5 hour Team Interaction Battery (TIB) task that occurred 5 times in-mission per crew. This resulted in a total of 13,058 conversational turns (i.e., 17.8% uplifting, 3.3% discouraging, 75.76% neutral, 3.14% nulls). Our findings with the revised behavioral coding and ML/NLP models indicate a 43.66% macro F1-score (i.e., 38.29% precision (P), 50.8% recall (R)) for a dialog state-tracking model that includes information from the sender only, and a 40.9% F1-score (i.e., 38.7% P, 43.36% R) for the same model that includes information from both the sender and the target of the micro-behavior. These are significantly higher compared to simple random forest models that classify behaviors strictly based on speech content and do not consider iterative team dynamics, achieving a 36.07% F1-score (i.e., 39.04% R, 33.53% P). Our findings demonstrate potential ways to leverage large conversational datasets to better capture complex team dynamics. We will discuss future directions including proposed models that can incorporate additional mission days and tasks beyond the TIB for objectively quantifying team behavior at high temporal resolution in space exploration missions.

Projna Paromita↗

NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗

NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over 60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗

NASA NTRS2001

Control of a simulated arm using a novel combination of Cerebellar learning mechanisms

We present a model of cerebellar cortex that combines two types of learning: feedforward predicitve association based on local Hebbian-type learning between granule cell ascending branch and parallel fiber inputs, and reinforcement learning with feedback error correction based on climbing fiber activity.

cerebellum cerebellar learning dynamic state estim↗

NASA NTRS2017

UAV Trajectory Modeling Using Neural Networks

Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural network should be able to predict the vehicle's future states at next time step. A complete 4-D trajectory are then generated step by step using the trained neural network. Experiments in this work show that the neural network can approximate the sUAV's model and predict the trajectory accurately.

Neural Networks↗

NASA NTRS1992

Implementations of learning control systems using neural networks

The systematic storage in neural networks of prior information to be used in the design of various control subsystems is investigated. Assuming that the prior information is available in a certain form (namely, input/output data points and specifications between the data points), a particular neural network and a corresponding parameter design method are introduced. The proposed neural network addresses the issue of effectively using prior information in the areas of dynamical system (plant and controller) modeling, fault detection and identification, information extraction, and control law scheduling.

Sartori, Michael A.↗

NASA NTRS2015

Modeling of Local BEAM Structure for Evaluation of MMOD Impacts to Support Development of a Health Monitoring System

This report summarizes initial modeling of the local response of the Bigelow Expandable Activity Module (BEAM) to micrometeorite and orbital debris (MMOD) impacts using a structural, non-linear, transient dynamic finite element code. Complementary test results for a local BEAM structure are presented for both hammer and projectile impacts. Review of these data provided guidance for the transient dynamic model development. The local model is intended to support predictions using the global BEAM model, described in a companion report. Two types of local models were developed. One mimics the simplified Soft-Goods (fabric envelop) part of the BEAM NASTRAN model delivered by the project. The second investigates through-the-thickness modeling challenges for MMOD-type impacts. Both the testing and the analysis summaries contain lessons learned and areas for future efforts.

Lyle, Karen H.↗

NASA NTRS1990

A Petri-net coordination model for an intelligent mobile robot

The authors present a Petri net model of the coordination level of an intelligent mobile robot system (IMRS). The purpose of this model is to specify the integration of the individual efforts on path planning, supervisory motion control, and vision systems that are necessary for the autonomous operation of the mobile robot in a structured dynamic environment. This is achieved by analytically modeling the various units of the system as Petri net transducers and explicitly representing the task precedence and information dependence among them. The model can also be used to simulate the task processing and to evaluate the efficiency of operations and the responsibility of decisions in the coordination level of the IMRS. Some simulation results on the task processing and learning are presented.

Wang, F.-Y.↗

NASA NTRS2010

Operational Lessons Learned from the Ares I-X Flight Test

The Ares I-X flight test, launched in 2009, is the first test of the Ares I crew launch vehicle. This development flight test evaluated the flight dynamics, roll control, and separation events, but also provided early insights into logistical, stacking, launch, and recovery operations for Ares I. Operational lessons will be especially important for NASA as the agency makes the transition from the Space Shuttle to the Constellation Program, which is designed to be less labor-intensive. The mission team itself comprised only 700 individuals over the life of the project compared to the thousands involved in Shuttle and Apollo missions; while missions to and beyond low-Earth orbit obviously will require additional personnel, this lean approach will serve as a model for future Constellation missions. To prepare for Ares I-X, vehicle stacking and launch infrastructure had to be modified at Kennedy Space Center's Vehicle Assembly Building (VAB) as well as Launch Complex (LC) 39B. In the VAB, several platforms and other structures designed for the Shuttle s configuration had to be removed to accommodate the in-line, much taller Ares I-X. Vehicle preparation activities resulted in delays, but also in lessons learned for ground operations personnel, including hardware deliveries, cable routing, transferred work and custodial paperwork. Ares I-X also proved to be a resource challenge, as individuals and ground service equipment (GSE) supporting the mission also were required for Shuttle or Atlas V operations at LC 40/41 at Cape Canaveral Air Force Station. At LC 39B, several Shuttle-specific access arms were removed and others were added to accommodate the in-line Ares vehicle. Ground command, control, and communication (GC3) hardware was incorporated into the Mobile Launcher Platform (MLP). The lightning protection system at LC 39B was replaced by a trio of 600-foot-tall towers connected by a catenary wire to account for the much greater height of the vehicle. Like Shuttle, Ares I-X will be stacked on a MLP and rolled out to the pad on a Saturn-era crawler-transporter. While Ares I-X was only held in place by the four hold-down posts on its aft skirt during rollout, a new vehicle stabilization system (VSS) attached to the vertical service structure kept the vehicle from undue swaying prior to launch at the pad, LC 39B. Following the launch, the flight test vehicle first stage was recovered with the aid of new parachutes resized to accommodate the five-segment-long first stage, which had a much greater length and mass than the Shuttle s reusable solid rocket boosters. After splashdown, recovery divers exercised extra care when handling the first stage to ensure that the flight data recorders in the fifth segment simulator were not damaged by exposure to sea water. The data recovered from the Ares I-X flight test will be very valuable in verifying the predicted environments and models used to design the vehicle. Lessons learned from Ares I-X will be shared with the Ares Projects through written and verbal reports and through integration of mission team members into the Project workforce.

Davis, Stephan R.↗

NASA NTRS1994

Self-calibrating models for dynamic monitoring and diagnosis

The present goal in qualitative reasoning is to develop methods for automatically building qualitative and semiquantitative models of dynamic systems and to use them for monitoring and fault diagnosis. The qualitative approach to modeling provides a guarantee of coverage while our semiquantitative methods support convergence toward a numerical model as observations are accumulated. We have developed and applied methods for automatic creation of qualitative models, developed two methods for obtaining tractable results on problems that were previously intractable for qualitative simulation, and developed more powerful methods for learning semiquantitative models from observations and deriving semiquantitative predictions from them. With these advances, qualitative reasoning comes significantly closer to realizing its aims as a practical engineering method.

Kuipers, Benjamin↗

NASA NTRS1995

Earth-space links and fade-duration statistics

In recent years, fade-duration statistics have been the subject of several experimental investigations. A good knowledge of the fade-duration distribution is important for the assessment of a satellite communication system's channel dynamics: What is a typical link outage duration? How often do link outages exceeding a given duration occur? Unfortunately there is yet no model that can universally answer the above questions. The available field measurements mainly come from temperate climatic zones and only from a few sites. Furthermore, the available statistics are also limited in the choice of frequency and path elevation angle. Yet, much can be learned from the available information. For example, we now know that the fade-duration distribution is approximately lognormal. Under certain conditions, we can even determine the median and other percentiles of the distribution. This paper reviews the available data obtained by several experimenters in different parts of the world. Areas of emphasis are mobile and fixed satellite links. Fades in mobile links are due to roadside-tree shadowing, whereas fades in fixed links are due to rain attenuation.

Davarian, Faramaz↗

NASA NTRS1996

Earth-Space Links and Fade-Duration Statistics

Davarian, Faramaz↗