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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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Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
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A Deep Learning Pipeline for Optimizing Large-scale Phase Field Simulations
Phase field (PF) simulations are computationally expensive but remain a key analysis tool to understand the complex mechanisms of additive manufacturing (AM) processes. Each PF simulation-aided analysis requires thousands of node hours on leadership-class supercomputers. One of the main goals of these analyses is the study of microstructure evolution during the build process which begins with the onset of nucleation. Nucleation occurs under certain thermomechanical conditions which are not known a priori and many PF simulations are required to identify ranges of input thermo-mechanical parameters that can result in the onset of nucleation. Since many of the simulations do not result in nucleation, an analysis campaign often ends up wasting tremendous amounts of precious computing resources executing nucleation-absent simulations. The goal of this work is to design and train deep learning models to inform a PF simulation about the likelihood of the occurrence of nucleation in a future simulation time-step based on the state summary over a finite number of past time-steps of a running simulation. If the prediction determines that the running simulation is unlikely to reach nucleation in the allotted time, then its execution is stopped immediately ultimately resulting in vast reduction in wasted computations when accrued over all the PF simulations typically performed in a single or multiple analysis campaign(s). The paper presents the performance of a machine learning pipeline that uses a convolutional neural network (CNN) model to learn an embedding which is then used with a self-attention network to build a multi-task deep learning model to predict the likelihood of nucleation. The model also predicts the input parameters used in a simulation. Performance is compared with a baseline pipeline that uses an off-the-shelf LeNet-5 model to learn the initial embedding. Despite their smaller size, performance results indicate significant improvement in accuracy of the proposed models compared to the larger baseline models.
LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics
Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features. Beyond training, examining the loss function with respect to a network's parameters (i.e., as a loss landscape) can reveal insights into the architecture and learning process. While the local structure of the loss landscape surrounding an individual solution can be characterized using a variety of approaches, the global structure of a loss landscape, which includes potentially many local minima corresponding to different solutions, remains far more difficult to conceptualize and visualize. To address this difficulty, we introduce LossLens, a visual analytics framework that explores loss landscapes at multiple scales. LossLens integrates metrics from global and local scales into a comprehensive visual representation, enhancing model diagnostics. Here we demonstrate LossLens through two case studies: visualizing how residual connections influence a ResNet-20, and visualizing how physical parameters influence a physics-informed neural network (PINN) solving a simple convection problem.
Solving classical inference problems on modern machine-learning platforms [Slides]
The outline for this presentation includes: Classical inference (i.e., learning) problems in dynamical systems (through examples), Adjoint state methods, Machine learning for time series data, and Examples characterizing three classes of inference/learning problems.
Uncertainty Quantification of Classifying Mechanical Breach in the Crash and Burn Problem
The prediction of mechanical breach in the Crash and Burn problem is important for designing the full system to minimize the probability of loss of containment. Due to uncertainties in the impact angle, material properties, and material models, it is not possible to define an exact critical impact speed at which the system breaches. Furthermore, the cost of running a large scale sampling study to determine the empirical probability of breach is prohibitive. In this work, surrogate models from machine learning, namely logistic regression and artificial neural networks, are introduced to predict binary classification of pass versus breach from a limited set of samples. The structure and calibration of these classifiers is discussed, and a set of metrics for describing the performance of the classifiers is introduced. The classifiers are used on the UUR version of the Crash model to attempt prediction of failure probability and perform variable sensitivity analysis. Where a single sample of the computational model can take hundreds of CPU-hours, training and evaluating a classifier can take seconds or less, thus giving high predictive power in a relatively short time.
Cyber Resilient Fossil Fuel Power Plants and Control Systems of the Future. Final Report Highlighting Recommendations
The following report proposes how GE Research, NETL and energy utility partners could advance these efforts and improve the current state of the art of cybersecurity for U.S. fossil generation and systems to provide a more holistic approach to identify, protect, detect, respond, recover and endure from existing and future cyber threats.
Understanding the Physics Representation of Deep Learning Models in Environmental Applications
Deep learning (DL) models have been popular in earth and environmental modeling and analysis, which exhibit huge potential in capturing and reconstructing the non-linearity of relevant environmental processes. They are extensively used as analytical tools or emulators for multiple domains (atmosphere, land surface, ocean, and biogeochemistry). Despite their success, their internal working mechanism remains largely unknown. Such a lack of knowledge hinders the identification of physically consistent models that are fully adaptive to non-stationary climate, as well as the development of physics-informed machine learning such as physics-informed neural network (PINN). To establish preliminary knowledge and framework of such physics representation evaluation, this project focuses on an improved understanding of DL models in the environmental applications. DL models are increasingly applied to environmental modeling and prediction. However, they have been evaluated mostly from a performance perspective, and there is a gap in understanding how they represent the known physics internally. Such knowledge is especially critical when applying DL models under climate change conditions, where new inputs are likely outside the ranges of the training datasets. In this project, we reveal how the known physical processes are represented within DL models from both statistical and mechanistic perspectives. Leveraging the traditional model evaluations that focus more on the accuracies of predictions, we establish a framework that examines both the accuracy and physics representation of DL models. This analysis framework can identify DL models that make the correct predictions based on correct physics, thus enhancing the existing explainable artificial intelligence (explainable-AI) portfolio. It lays a foundation for developing novel metrics to evaluate the emerging DL models in environmental applications. This knowledge also informs the development of physics-informed DL models by revealing the direct connections between the known physical processes and specific model components or structures.
Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data
This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two natural disasters taken into consideration. The Climate Change Dataset was created by compiling various open-access data sources. This dataset contains 6334 aerial images from UAV (unmanned aerial vehicles) images and satellite images. The Climate Change Dataset was then used to train Deep Learning (DL) models to identify natural disasters. Four different Machine Learning (ML) models were used: convolutional neural network (CNN), DenseNet201, VGG16, and ResNet50. These ML models were trained on our Climate Change Dataset so that their performance could be compared. DenseNet201 was chosen for optimization. All four ML models performed well. DenseNet201 and ResNet50 achieved the highest testing accuracies of 99.37% and 99.21%, respectively. This research project demonstrates the potential of AI to address environmental challenges, such as climate change-related natural disasters. This study’s approach is novel by creating a new dataset, optimizing an ML model, cross-validating, and presenting desertification as one of our natural disasters for DL detection. Three categories were used (Flooded, Desert, Neither). Our study relates to AI for Climate Change and Environmental Sustainability. Drone emergency response would be a practical application for our research project.
Uncovering Structure–Conductivity Relationships in Anion Exchange Membranes (AEMs) Using Interpretable Machine Learning
Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure–property relationships. In this study, we introduce a machine learning framework that leverages conditional graph neural networks (cGNNs) and descriptor-based models and a hybrid graph neural network (HGARE) to predict and interpret ionic conductivity. The descriptor-based pipeline employs principal component analysis (PCA), ablation, and SHAP analysis to identify factors governing anion conductivity, revealing electronic, topological, and compositional descriptors as key contributors. Beyond prediction, dimensionality reduction and clustering are performed by employing t-SNE and KMeans as well as SOM, which reveal distinct membranes clusters, some of which were enriched with high anion conductivity. Among graph-based approaches, the graph convolutional (GCN) achieved strong predictive performance, while the Hybrid Graph Autoencoder-Regressor Ensemble (HGARE) achieved the highest accuracy. Additionally, atom-level saliency maps from GCN provide spatial explanations for conductive behavior, revealing the importance of polarizable and flexible regions. This work contributes to the accelerated and data-driven design of high-performance AEMs.
Acquisition and production of skilled behavior in dynamic decision-making tasks
Detailed summaries of two NASA-funded research projects are provided. The first project was an ecological task analysis of the Star Cruiser model. Star Cruiser is a psychological model designed to test a subject's level of cognitive activity. Ecological task analysis is used as a framework to predict the types of cognitive activity required to achieve productive behavior and to suggest how interfaces can be manipulated to alleviate certain types of cognitive demands. The second project is presented in the form of a thesis for the Masters Degree. The thesis discusses the modeling of decision-making through the use of neural network and genetic-algorithm machine learning technologies.
Advances in Medical Analytics Solutions for Autonomous Medical Operations on Long-Duration Missions
A review will be presented on the progress made under STMDGame Changing Development Program Funding towards the development of a Medical Decision Support System for augmenting crew capabilities during long-duration missions, such as Mars Transit. To create an MDSS, initial work requires acquiring images and developing models that analyze and assess the features in such medical biosensor images that support medical assessment of pathologies. For FY17, the project has focused on ultrasound images towards cardiac pathologies: namely, evaluation and assessment of pericardial effusion identification and discrimination from related pneumothorax and even bladder-induced infections that cause inflammation around the heart. This identification is substantially changed due to uncertainty due to conditions of fluid behavior under space-microgravity. This talk will present and discuss the work-to-date in this Project, recognizing conditions under which various machine learning technologies, deep-learning via convolutional neural nets, and statistical learning methods for feature identification and classification can be employed and conditioned to graphical format in preparation for attachment to an inference engine that eventually creates decision support recommendations to remote crew in a triage setting.
Planetary Boundary Layer Height Estimates From ICESat-2 and CATS Backscatter Measurements
The lowest layer of the atmosphere in which all human activity occurs is called the Planetary Boundary Layer (PBL). All physical interactions with the surface, such as heat and moisture transport, pollution dispersion and transport happen in this relatively shallow layer. The ability to understand and model the complex interactions that occur in the PBL is very important to air quality, weather prediction and climate modeling. A fundamental and physically important property of the PBL is its thickness or height. This work presents two methods to obtain global PBL height using satellite lidar data from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and the Cloud-Aerosol Transport System (CATS). The first method is a straightforward backscatter threshold technique and the second is a machine learning approach known as a Convolutional Neural Network. The PBL height retrievals from the two methods are compared with each other and with PBL height from the NASA GEOS MERRA-2 reanalysis. The lidar-retrieved PBL heights have a high degree of spatial correlation with the model heights but are generally higher over ocean (∼400 m) and over northern hemisphere high latitude regions (∼1,000 m). Over mid-latitude and tropical land areas, the satellite estimated PBL heights agree well with model mid-day estimates. This work demonstrates the feasibility of using satellite lidar backscatter measurements to obtain global PBL height estimates, as well as determining seasonal and regional variability of PBL height.
Science Autonomy for Ocean Worlds Astrobiology: A Perspective
Astrobiology missions to ocean worlds in our solar system must overcome both scientific and technological challenges due to extreme temperature and radiation conditions, long communication times, and limited bandwidth. While such tools could not replace ground-based analysis by science and engineering teams, machine learning algorithms could enhance the science return of these missions through development of autonomous science capabilities. Examples of science autonomy include onboard data analysis and subsequent instrument optimization, data prioritization (for transmission), and real-time decision-making based on data analysis. Similar advances could be made to develop streamlined data processing software for rapid ground-based analyses. Here we discuss several ways machine learning and autonomy could be used for astrobiology missions, including landing site selection, prioritization and targeting of samples, classification of “features” (e.g., proposed biosignatures) and novelties (uncharacterized, “new” features, which may be of most interest to agnostic astrobiological investigations), and data transmission.
Flux Improvement based on Machine Learning for the CERES FluxByCldTyp Data Product
The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.
Improvement of Radiative Fluxes for the CERES FluxByCldTyp Data Product Based on Machine Learning Technique
The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere and surface flux data for climate studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models. The FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate their broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Preliminary results show significant LW improvement.
Improvement of Radiative Fluxes for the CERES FluxByCldTyp Data Product Based on Machine Learning Technique
The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.
Accelerated Machine Learning for High Energy Physics
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