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NASA NTRSDate not supplied

Prediction of High-Latitude Ionospheric Electrodynamics Using the Machine Learning Based Auroral Ionospheric Electrodynamics Model

We introduce a new framework for Machine-Learning (ML) based Auroral Ionosphere Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents (FACs) of Kunduri et al., 2020 (https://doi.org/10.1029/2020JA027908), the FAC-derived aurora conductance model of Robinson et al., 2020 (https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen & Brekke (1993). The ML-AIM inputs are 60min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pederson/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity on 14 May 2013 and a geomagnetic storm on 7-8 September 2017. ML-AIM produces reasonable ionospheric potential patterns such as two cell convection patterns and the enhancement of electric potentials during active times. The cross polar cap potential drop from ML-AIM is also comparable to the ones from the Weimer 2005 model, Super Dual Auroral Radar Network (SuperDARN), and Defense Meteorological Satellite Program (DMSP) F17 observations. ML-AIM is unique in a sense that it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while other traditional empirical model like Weimer 2005 is designed to provide static ionospheric conditions under steady solar wind/IMF conditions. In future, ML-AIM will include ML-based models of aurora precipitation and ionospheric conductance, improving its performance during active times.

H. K. Connor↗

NASA NTRS2024

Calculating the High-Latitude Ionospheric Electrodynamics Using A Machine Learning-Based Field-Aligned Current Model

We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC-derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML-AIM inputs are 60-min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML-AIM produces physically accurate ionospheric potential patterns such as the two-cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML-AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data-assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML-AIM than others. ML-AIM is unique and innovative because it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi-static ionospheric condition under quasi-steady solar wind/IMF conditions. Plans are underway to improve ML-AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.

auroral electrodynamics↗

NASA NTRS2010

TEACH (Train to Enable/Achieve Culturally Sensitive Healthcare)

Personnel from diverse ethnic and demographic backgrounds come together in both civilian and military healthcare systems, facing diagnoses that at one level are equalizers: coronary disease is coronary disease, breast cancer is breast cancer. Yet the expression of disease in individuals from different backgrounds, individual patient experience of disease as a particular illness, and interactions between patients and providers occurring in any given disease scenario, all vary enormously depending on the fortuity of the equation of "which patient happens to arrive in whose exam room." Previously, providers' absorption of lessons-learned depended on learning as an apprentice would when exposed over time to multiple populations. As a result, and because providers are often thrown into situations where communications falter through inadequate direct patient experience, diversity in medicine remains a training challenge. The questions then become: Can simulation and virtual training environments (VTEs) be deployed to short-track and standardize this sort of random-walk problem? Can we overcome the unevenness of training caused by some providers obtaining the valuable exposure to diverse populations, whereas others are left to "sink or swim"? This paper summarizes developing a computer-based VTE called TEACH (Training to Enable/Achieve Culturally Sensitive Healthcare). TEACH was developed to enhance healthcare providers' skills in delivering culturally sensitive care to African-American women with breast cancer. With an authoring system under development to ensure extensibility, TEACH allows users to role-play in clinical oncology settings with virtual characters who interact on the basis of different combinations of African American sub-cultural beliefs regarding breast cancer. The paper reports on the roll-out and evaluation of the degree to which these interactions allow providers to acquire, practice, and refine culturally appropriate communication skills and to achieve cultural and individual personalization of healthcare in their clinical practices.

Maulitz, Russell↗

NASA NTRS1993

Adaptive fuzzy system for 3-D vision

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

Mitra, Sunanda↗

NASA NTRS1992

Discrete time learning control in nonlinear systems

In this paper digital learning control methods are developed primarily for use in single-input, single-output nonlinear dynamic systems. Conditions for convergence of the basic form of learning control based on integral control concepts are given, and shown to be satisfied by a large class of nonlinear problems. It is shown that it is not the gross nonlinearities of the differential equations that matter in the convergence, but rather the much smaller nonlinearities that can manifest themselves during the short time interval of one sample time. New algorithms are developed that eliminate restrictions on the size of the learning gain, and on knowledge of the appropriate sign of the learning gain, for convergence to zero error in tracking a feasible desired output trajectory. It is shown that one of the new algorithms can give guaranteed convergence in the presence of actuator saturation constraints, and indicate when the requested trajectory is beyond the actuator capabilities.

Longman, Richard W.↗

NASA NTRS1993

Application of hardness testing in foundry processing operations: A university and industry partnership

The objectives are the following: (1) to plan a tour of local manufacturing facility to observe hardness testing methods used and how they support the manufacturing process today; (2) to learn the operation of Brinell Hardness Testing and equipment set up with samples of accessory equipment for testing of various materials; (3) to use a Brinell scope (hand magnifier) to measure the diameter of the test indentation (in millimeters); (4) to use the hardness equation to determine the spherical area of the indentation and the calculation of the Brinell Hardness Number; (5) to use standard charts for ASTM standard size indenters to find the hardness number; and (6) to learn a method of conversion of the Brinell number to other hardness scales, their common usage, and also to convert the hardness number to the approximate tensile strength.

Martin, Donald H.↗

NASA NTRS1992

Learning a trajectory using adjoint functions and teacher forcing

A new methodology for faster supervised temporal learning in nonlinear neural networks is presented which builds upon the concept of adjoint operators to allow fast computation of the gradients of an error functional with respect to all parameters of the neural architecture, and exploits the concept of teacher forcing to incorporate information on the desired output into the activation dynamics. The importance of the initial or final time conditions for the adjoint equations is discussed. A new algorithm is presented in which the adjoint equations are solved simultaneously (i.e., forward in time) with the activation dynamics of the neural network. We also indicate how teacher forcing can be modulated in time as learning proceeds. The results obtained show that the learning time is reduced by one to two orders of magnitude with respect to previously published results, while trajectory tracking is significantly improved. The proposed methodology makes hardware implementation of temporal learning attractive for real-time applications.

Toomarian, Nikzad B.↗

NASA NTRS2004

Cloud-Vegetation Radiative Interaction: What Can We Learn from it about Cloud Optical Properties?

Because of their radiative interactions, the vegetation canopy and the atmosphere are coupled together; each serves as a boundary condition to the radative transfer equations in the adjacent medium. To better understand radiative processes in these media we need an accurate description of their interactions. This presentation outlines a technique needed to describe interactions between vegetation and clouds and exploits it to retrieve cloud optical depth from ground-based radiance measurements. measurements of zenith radiance in the RED and NIR spectral regions. In addition to an algebraic combinations of spectral radiances such as Normalized Difference Cloud Index (NDCI) that can result in poor retrievals due to its insensitivity to cloud fraction, both RED and NIR radiances as points on the "RED vs. NIR" plane are proposed to be used for retrieval. The proposed method is applied to a multi-channel sunphotometer (as a part of AERONET) measurements at the ARM site in Oklahoma. In addition to cloud optical depth, the new method also infers a "radiatively effective" cloud fraction.

Marshak, Alexander↗

NASA NTRS1985

Simulation as an aid to phenomenological modeling

Certain difficulties arise in connection with the development of models for the prediction of turbulent flow characteristics. The present paper is mainly concerned with an approach which provides an aid in such development work by making it possible to test Reynolds average turbulence models. This approach is based on full and large eddy simulations of turbulent flows. Attention is given to details regarding Reynolds average turbulence models, mixing length models, two equation (k-epsilon) models, Reynolds stress models, and passive scalars. It is shown that a great deal about modeling can be learned from simulations of turbulent flows. There is reason to believe that these results will lead to improvements in the quality of predictions of turbulent flows.

Ferziger, J. H.↗

NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗

NASA NTRSDate not supplied

Machine Learning Approach for Aircraft Performance Model Parameter Estimation for Trajectory Prediction Applications

Inaccurate prediction of aircraft trajectory by ground-based decision support tools (DST) is a major concern in air traffic management (ATM). Aircraft trajectory prediction tools rely on a simplified point-mass aircraft performance model (APM) to make their predictions. Even though the performance coefficients and weight of an aircraft are a vital part of the APM’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to DSTs. Current ATM research focuses on improving the estimate of some APM parameters by freezing all other coefficients. This simplified approach introduces unwanted sources of bias and negatively impacts the accuracy of the performance model. In this paper, we apply machine learning (ML) techniques for the simultaneous prediction of three key APM parameters (two drag coefficients and the initial aircraft weight). To accomplish this, we employ an ordinary differential equation (ODE) fitting approach to generate optimized APM parameter labels customized to each individual flight record. Subsequently, we train ML models to capture the relationship between the historical data and the optimized APM parameters. Two different ML model solutions are applied and APM coefficients are predicted for unseen flights. The results indicate that the ML models are able to capture the relationship between APM parameters and flight-related features with good accuracy.

trajectory prediction, machine learning, aircraft ↗

NASA NTRS1986

What have we learned about galaxies through large computations?

Numerical models permit modelling processes within galaxies that are seemingly beyond the reach of physical contact or close-up viewing from different angles. N-body equations permit simple computations of the interactions of individual stars within galaxies, but the motions of these objects feature nonlinear characteristics because of the forces exerted by the complex as a whole which are not amenable to examination with Newtonian laws of force. The models have, however, revealed that thin disk galactic are not stable forms, that barlike structures rotating end-over-end are more stable structures, and that galactic contraction occurs during collisions between galaxies. Internal effects have been successfully predicted for galaxies which are part of clusters, growth rate disturbances due to the expansion of the Universe have been quantified as 2-3 percent and dynamical friction has been shown to damage galaxies which are sliding into background material.

Miller, R. H.↗

NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Generative Machine Learning↗

NASA NTRS2006

Estimation of Stability and Control Derivatives of an F-15

A technique for real-time estimation of stability and control derivatives (derivatives of moment coefficients with respect to control-surface deflection angles) was used to support a flight demonstration of a concept of an indirect-adaptive intelligent flight control system (IFCS). Traditionally, parameter identification, including estimation of stability and control derivatives, is done post-flight. However, for the indirect-adaptive IFCS concept, parameter identification is required during flight so that the system can modify control laws for a damaged aircraft. The flight demonstration was carried out on a highly modified F-15 airplane (see Figure 1). The main objective was to estimate the stability and control derivatives of the airplane in nearly real time. A secondary goal was to develop a system to automatically assess the quality of the results, so as to be able to tell a learning neural network which data to use. Parameter estimation was performed by use of Fourier-transform regression (FTR) a technique developed at NASA Langley Research Center. FTR is an equation- error technique that operates in the frequency domain. Data are put into the frequency domain by use of a recursive Fourier transform for a discrete frequency set. This calculation simplifies many subsequent calculations, removes biases, and automatically filters out data beyond the chosen frequency range. FTR as applied here was tailored to work with pilot inputs, which produce correlated surface positions that prevent accurate parameter estimates, by replacing half the derivatives with predicted values. FTR was also set up to work only on a recent window of data, to accommodate changes in flight condition. A system of confidence measures was developed to identify quality-parameter estimates that a learning neural network could use. This system judged the estimates primarily on the basis of their estimated variances and of the level of aircraft response. The resulting FTR system was implemented in the Simulink software system and auto-coded in the C programming language for use on the Airborne Research Test System (ARTS II) computer installed in the F-15 airplane. The Simulink model was also used in a control room that utilizes the Ring Buffered Network Bus hardware and software, making it possible to evaluate test points during flights. In-flight parameter estimation was done for piloted and automated maneuvers, primarily at three test conditions. Figure 2 shows results for pitching moment due to symmetric stabilator actuations for a series of three pitch doublet maneuvers (in a doublet maneuver, a command to change attitude in a given direction by a given amount is followed immediately by a command to change attitude in the opposite direction by the same amount). A time window of 5 seconds was used. The portions of the curves shown in red are those that passed the confidence tests. The technique showed good convergence for most derivatives for both kinds of maneuvers - typically within a few seconds. The confidence tests were marginally successful, and it would be necessary to refine them for use in an IFCS.

Smith, Mark↗

NASA NTRS1995

Numerical solution of differential equations by artificial neural networks

Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks (ANN's) are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

Meade, Andrew J., Jr.↗

NASA NTRSDate not supplied

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗

NASA NTRSDate not supplied

Physics Informed Neural Nets for Systems Health Management

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Development in data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. The research work presents application of physics-informed neural nets application to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage.

Physics Informed↗

NASA NTRS2023

Sub-Continental-Scale Carbon Stocks of Individual Trees in African Drylands

The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0–1,000 mm year −1 rainfall zone by coupling field data, machine learning, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha −1 and 63 kg C tree −1 in the arid zone to 3.7 Mg C ha −1 and 98 kg tree −1 in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies23 for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.

Compton Tucker↗