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NASA NTRSDate not supplied

Crew Performance Support System to Aid in Anomaly Resolution: Concept of Operations

As missions progress into deep space, communication delays and disruptions will disenable the crew’s reliance on Earth experts. There are also limitations in the amount of data that can be downlinked to the ground. It is prudent to assume that critical, complex vehicle or habitat sub-systems will malfunction at a time when a lunar or Mars’ crew cannot rely on the Earth-Support team to detect, diagnose and resolve the problem and it is impractical to expect a small crew to step-in with the same level of expertise as 50+ authorities. The crew will need novel processes and advanced technological support to independently identify and resolve safety- and time-critical anomalies. That a self-reliant crew is unable to respond appropriately to time-critical anomalies is a significant risk to crew safety and mission success. This risk is driven by several factors; novel and unanticipated anomalies would not have been trained pre-flight, the crew could forget their pre-flight training or spaceflight stressors could impair the crew’s problem-solving ability. At last year’s IWS, Beard reported that a single spaceflight stressor (elevated CO2) could undermine the crew’s ability to independently respond to emergencies. Concept of Operations (ConOps) provide a common view of future system functions to all stakeholders. For the current project, a ConOps was developed that describes the operational processes, practices and capabilities needed by a crew of astronauts on deep space missions to autonomously respond to anticipated and unanticipated anomalies. It is crucial to recognize that, as of August 2018 existing technologies are unable to effectively support crew anomaly response to unanticipated events. “Intelligent technology” has not reached a maturity level that permits generalizing a solution to novel situations. For example, to train intelligent technology requires volumes of data that do not exist. The complexities involved in a manned mission to Mars cannot be compared to sending rovers to Mars using scripted software. This ConOps proposes a Crew Performance Support System (CPSS) that will push NASA and its industry partners toward what will be required for a safe and successful manned mission to Mars. Anomaly resolution during a deep space mission will take place within a dynamic, or changing, context. The figure to the left shows five broad contextual variables: the organizational culture, mission context, system characteristics, team characteristics and individual characteristics. The yellow arrow indicates that spaceflight and task-related stressors can affect system, team and individual crewmember characteristics and therefore anomaly response potential. The figure depicts a protective umbrella of Human-System Integration (HSI) principles that should be instituted during CPSS development including a balanced workload, shared situation awareness and building an appropriate level of trust in the automation. The figure also depicts two interrelated and cooperative components, an HSI Data System and other Enabling Capabilities will be required to support crew anomaly response and Earth-Support situation awareness. As we journey from ISS to Gateway to Mars, multiple, simultaneous and integrated research and development efforts (i.e., support systems co-evolution) must be implemented to meet the problem-solving challenges a self-reliant crew will face on a Mars’ mission. The crossovers between the capabilities are just as important as the discrete capabilities themselves. As the capabilities mature, the lines between the support subdomains will blur and an integrated system will emerge. The ConOps summarizes current knowledge about how highly trained people solve anomalies in safety- and time-critical situations, describes a group of capabilities that could help to reduce the extant risk and documents requirements levied on additional systems that provides critical inputs to the CPSS. Scenarios are used to promote a shared understanding of processes, practices and technological goals needed for safe and productive manned missions beyond LEO.

HSIA risk↗
DOE OSTI2021

Predicting fault slip via transfer learning

Abstract Data-driven machine-learning for predicting instantaneous and future fault-slip in laboratory experiments has recently progressed markedly, primarily due to large training data sets. In Earth however, earthquake interevent times range from 10’s-100’s of years and geophysical data typically exist for only a portion of an earthquake cycle. Sparse data presents a serious challenge to training machine learning models for predicting fault slip in Earth. Here we describe a transfer learning approach using numerical simulations to train a convolutional encoder-decoder that predicts fault-slip behavior in laboratory experiments. The model learns a mapping between acoustic emission and fault friction histories from numerical simulations, and generalizes to produce accurate predictions of laboratory fault friction. Notably, the predictions improve by further training the model latent space using only a portion of data from a single laboratory earthquake-cycle. The transfer learning results elucidate the potential of using models trained on numerical simulations and fine-tuned with small geophysical data sets for potential applications to faults in Earth.

58 GEOSCIENCES↗
DOE OSTI2022

Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification

To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV-1 evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5–50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling and that adding biomarkers improves reconstructions of real HIV-1 transmission histories. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Evaluation of pre-training large language models on leadership-class supercomputers

Large language models (LLMs) have arisen rapidly to the center stage of artificial intelligence as the foundation models applicable to many downstream learning tasks. However, how to effectively build, train, and serve such models for many high-stake and first-principle-based scientific use cases are both of great interests and of great challenges. Moreover, pre-training LLMs with billions or even trillions of parameters can be prohibitively expensive not just for academic institutions, but also for well-funded industrial and government labs. Furthermore, the energy cost and the environmental impact of developing LLMs must be kept in mind. Here, in this work, we conduct a first-of-its-kind performance analysis to understand the time and energy cost of pre-training LLMs on the Department of Energy (DOE)’s leadership-class supercomputers. Employing state-of-the-art distributed training techniques, we evaluate the computational performance of various parallelization approaches at scale for a range of model sizes, and establish a projection model for the cost of full training. Our findings provide baseline results, best practices, and heuristics for pre-training such large models that should be valuable to HPC community at large. We also offer insights and optimization strategies for using the first exascale computing system, Frontier, to train models of the size of GPT-3 and beyond.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Medical Support for ISS Crewmember Training in Star City, Russia

Medical support of spaceflight training operations across international lines is a unique circumstance with potential applications to other aerospace medicine support scenarios. KBRwyle's Star City Medical Support Group (SCMSG) has fulfilled this role since the Mir-Shuttle era, with extensive experience and updates to share with the greater AsMA community. OVERVIEW: The current Soyuz training flow for assigned ISS crewmembers takes place in Star City, Russia. Soyuz training flow involves numerous activities that pose potential physical and occupational risks to crewmembers, including centrifuge runs and pressurized suit simulations at ambient and hypobaric pressures. In addition, Star City is a relatively remote location in a host nation with variable access to reliable, Western-standard medical care. For these reasons, NASA's Human Health & Performance contract allocates full-time physician support to assigned ISS crewmembers training in Star City. The Star City physician also treats minor injuries and illnesses as needed for both long- and short-term NASA support personnel traveling in the area, while working to develop and maintain relationships with local health care resources in the event of more serious medical issues that cannot be treated on-site. The specifics of this unique scope of practice will be discussed. SIGNIFICANCE: ISS crewmembers training in Star City are at potential physical and occupational risk of trauma or dysbarism during nominal Soyuz training flow, requiring medical support from an on-duty aerospace medicine specialist. This support maintains human health and performance by preserving crewmember safety and well-being for mission success; sharing information regarding this operational model may contribute to advances in other areas of international, military, and civilian operational aerospace medicine.

Chough, Natacha↗
NASA NTRS2017

Medical Support for ISS Crewmember Training in Star City, Russia

Medical support of spaceflight training operations across international lines is a unique circumstance with potential applications to other aerospace medicine support scenarios. KBRwyle's Star City Medical Support Group (SCMSG) has fulfilled this role since the Mir-Shuttle era, with extensive experience and updates to share with the greater AsMA community. OVERVIEW: The current Soyuz training flow for assigned ISS crewmembers takes place in Star City, Russia. Soyuz training flow involves numerous activities that pose potential physical and occupational risks to crewmembers, including centrifuge runs and pressurized suit simulations at ambient and hypobaric pressures. In addition, Star City is a relatively remote location in a host nation with variable access to reliable, Western-standard medical care. For these reasons, NASA's Human Health & Performance contract allocates full-time physician support to assigned ISS crewmembers training in Star City. The Star City physician also treats minor injuries and illnesses as needed for both long- and short-term NASA support personnel traveling in the area, while working to develop and maintain relationships with local health care resources in the event of more serious medical issues that cannot be treated on-site. The specifics of this unique scope of practice will be discussed. SIGNIFICANCE: ISS crewmembers training in Star City are at potential physical and occupational risk of trauma or dysbarism during nominal Soyuz training flow, requiring medical support from an on-duty aerospace medicine specialist. This support maintains human health and performance by preserving crewmember safety and well-being for mission success; sharing information regarding this operational model may contribute to advances in other areas of international, military, and civilian operational aerospace medicine.

Chough, Natacha↗
DOE OSTI2022

Dynamic three-dimensional maps of solute concentration and solute arrival times in synthetic and geologic porous media

Experimental and training data associated with the publication entitled ‘Three-Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography’ (published in WRR, DOI: 10.1029/2021WR031554). The dataset contain three-dimensional maps of various properties on four geologic rock cores obtained from X-ray Computed Tomography and Positron Emission Tomography imaging measurements. The synthetic training dataset used in the publication is also attached.

58 GEOSCIENCES↗
DOE OSTI2021

Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary

Recent advancements in scientific equipment and the adaptation of electronics and the Internet of Things (IoT) in our everyday lives resulted in large and complex data production at a high rate. Making meaningful and timely knowledge discovery at a modest cost from this big data is difficult for computing power and storage limitations. Training deep learning models incrementally in a streaming setting can help us with overcoming these limitations. However, in a well-known phenomenon named catastrophic forgetting, incrementally trained models increasingly perform poorly on the past data. To mitigate catastrophic forgetting in training in a streaming setting, we propose constructing a historical summary over time and use the summary with newly arrived data during incremental training. We propose various data summarization techniques such as random sampling, micro clustering, coreset computation, and Auto Encoders to counteract catastrophic forgetting. We built a pipeline for incremental training with a historical summary for training deep learning models for streaming data. We demonstrate the effectiveness of historical summary in mitigating catastrophic forgetting using three case studies involving three different deep learning applications: an Artificial Neural Network (ANN) for classification task on MNIST dataset, a language model (RNN-LM) on the WikiText2 dataset, and a Convolutional Neural Network (CNN), ResNet50 to classify the ImageNet dataset. Through the training of the models, we observe that catastrophic forgetting is evident in ANN and CNN but not in an RNN. For the first task, our method recovers up to 47.9% lost accuracy due to catastrophic forgetting. For the third task, the historical summary recovers classification accuracy by up to 25%. For the second task, though there is not proof of catastrophic forgetting, the training performance (PPL) improves by up to 26% with historical summary.

Dash, Sajal↗
NASA NTRS1994

The crash of P/Shoemaker-Levy 9 into Jupiter and its implications for comet bombardment on Earth

Periodic Comet Shoemaker/Levy 9 will impact Jupiter in late July 1994. The comet, which broke into more than 20 telescopically detectable fragments when it passed with the Roche lobe of Jupiter on July 8, 1992, is captured in a highly eccentric orbit about Jupiter. The 21 recognized nuclei will be spread out in a train of the order 7 x 10(exp 6) km long at the time of impact, and the impacts will be spread in time over about 5 1/2 days centered on about July 21.2 UT. In addition to the train of recognized bright nuclei, the comet consists of 'wings' of unresolved bodies that are the source of a very broad composite dust tail. The linear extent of the wings is about an order of magnitude greater than that of the train of recognized discrete nuclei. Collision of the wings will be spread in time over several months. Thus the impact of P/S-L 9 with Jupiter will be an event of appreciable duration.

Shoemaker, E. M.↗
DOE OSTI2025

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Detector pixel calibration of time-of-flight neutron diffractometers accelerated by machine learning

Modern time-of-flight neutron diffractometers at spallation neutron source are equipped with two dimensional detectors with fine pixelations. The flight path of neutrons from the moderator to the sample and to the detector needs to be precisely calibrated at detector pixel level using standard powders so the diffraction data from all the detector pixels can be correctly time-focused to produce high resolution diffraction peaks. The number of pixels can reach to millions which makes a single-pixel calibration process time-consuming, or even impossible, with conventional fitting routine. Here we presented a machine learning aided calibration process via a “training and predict” process by training machine learning models with the relations between the individual pixel time-of-flight diffraction pattern and fitted diffraction constant. The training models take a portion of the available pixels to predict the diffraction constants precisely and rapidly for massive pixel diffraction patterns.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1991

Kappa Group: The initial guess. A proposal in response to a commercial air transportation study

Kappa Aerospace presents their Aeroworld Aircraft, the Initial Guess (IG). This aircraft is designed to generate profit in the market which is currently controlled by the train and boat industry. The main priority of the design team was to develop an extremely efficient aircraft that could be sold at a reasonable price. The IG offers a quick and safe alternative to the existing means of transportation at a competitive price. The cruise velocity of 28 ft/sec. allows all flights to be between 20 and 45 minutes, which is a remarkable savings in time compared to travel by boat or train. The IG is propelled by a single Astro-05 engine with a Zinger 10-6 propeller. The Astro-05 is not an extremely powerful engine; however, it provides enough thrust to meet the design and safety requirements. The major advantage of the Astro-05 is that it is the most efficient engine available. The fuel efficiency of the Astro-05 is what puts the aircraft ahead of the competition. The money saved on an efficient engine can be passed on as lower ticket prices or increased revenue. The IG has a payload of 56 passengers and a wingspan of 7 ft. The 7 ft. wingspan allows the aircraft to fit into the gates of all of the cities that are targeted. Future endeavors of Kappa Aerospace will include fitting a stretch version of the IG with a larger propulsion system. This derivative aircraft will be able to carry more passengers and will be placed on the routes which have the greatest demand for travel. The fuselage and empennage are made of a wooden truss configuration, while the wing is made of a rib/spare configuration. The stress carrying elements are made of spruce, the nonstress carrying elements are made of balsa. The wing is removable for easy access into the fuselage. The easy access to the batteries will keep maintenance costs down.

Source record↗
NASA NTRS2019

System and Method for Training of State-Classifiers

Method and systems are disclosed for training state-classifiers for classification of cognitive state. A set of multimodal signals indicating physiological responses of an operator are sampled over a time period. A depiction of operation by the operator during the time period is displayed. In response to user input selecting a cognitive state for a portion of the time period, the one or more state-classifiers are trained. In training the state-classifiers, the set of multimodal signals sampled in the portion of the time period are used as input to the one or more state-classifiers and the selected one of the set of cognitive states is used as a target result to be indicated by the one or more state-classifiers.

Stephens, Chad L.↗
DOE OSTI2023

Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II

Deep neural network (DNN) potentials are an emerging tool for simulation of dynamical atomistic systems, with the promise of quantum mechanical accuracy at speedups of 10000$\times$. As with other DNN methods, hyperparameters used during training can make a substantial difference in model accuracy, and optimal settings vary with dataset. To enable rapid tuning of hyperparameters for DNN potential training, we developed a scalable multiobjective optimization evolutionary algorithm for supercomputers and tested it on the Summit system at the Oak Ridge Leadership Computing Facility (OLCF). The multiobjective approach is required due to the coupling of two learned values defining the potential: the energy and force. Using a large-scale implementation of the NSGA-II algorithm adapted for training DNN potentials, we discovered several optimal multiobjective combinations, including best choices of activation functions, learning rate scaling scheme, and pairing of the two radial cutoffs used in the three dimensional descriptor function.

Coletti, Mark↗
NASA NTRS2012

Virtual Guidance Ultrasound: A Tool to Obtain Diagnostic Ultrasound for Remote Environments

Astronauts currently acquire ultrasound images on the International Space Station with the assistance of real-time remote guidance from an ultrasound expert in Mission Control. Remote guidance will not be feasible when significant communication delays exist during exploration missions beyond low-Earth orbit. For example, there may be as much as a 20- minute delay in communications between the Earth and Mars. Virtual-guidance, a pre-recorded audio-visual tutorial viewed in real-time, is a viable modality for minimally trained scanners to obtain diagnostically-adequate images of clinically relevant anatomical structures in an autonomous manner. METHODS: Inexperienced ultrasound operators were recruited to perform carotid artery (n = 10) and ophthalmic (n = 9) ultrasound examinations using virtual guidance as their only instructional tool. In the carotid group, each each untrained operator acquired two-dimensional, pulsed, and color Doppler of the carotid artery. In the ophthalmic group, operators acquired representative images of the anterior chamber of the eye, retina, optic nerve, and nerve sheath. Ultrasound image quality was evaluated by independent imaging experts. RESULTS: Eight of the 10 carotid studies were judged to be diagnostically adequate. With one exception the quality of all the ophthalmic images were adequate to excellent. CONCLUSION: Diagnostically-adequate carotid and ophthalmic ultrasound examinations can be obtained by untrained operators with instruction only from an audio/video tutorial viewed in real time while scanning. This form of quick-response-guidance, can be developed for other ultrasound examinations, represents an opportunity to acquire important medical and scientific information for NASA flight surgeons and researchers when trained medical personnel are not present. Further, virtual guidance will allow untrained personnel to autonomously obtain important medical information in remote locations on Earth where communication is difficult or absent.

Caine,Timothy L.↗
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↗