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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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DOE OSTI2022

High-dimensional anomaly detection with radiative return in $\mathcal{e}$⁺$\mathcal{e}$⁻ collisions

Experiments at a future $\mathcal{e}$⁺$\mathcal{e}$⁻ collider will be able to search for new particles with masses below the nominal centre-of-mass energy by analyzing collisions with initial-state radiation (radiative return). We show that machine learning methods that use imperfect or missing training labels can achieve sensitivity to generic new particle production in radiative return events. In addition to presenting an application of the classification without labels (CWoLa) search method in $\mathcal{e}$⁺$\mathcal{e}$⁻ collisions, our study combines weak supervision with variable-dimensional information by deploying a deep sets neural network architecture. We have also investigated some of the experimental aspects of anomaly detection in radiative return events and discuss these in the context of future detector design.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2012

STAR-GALAXY CLASSIFICATION IN MULTI-BAND OPTICAL IMAGING

Ground-based optical surveys such as PanSTARRS, DES, and LSST will produce large catalogs to limiting magnitudes of r {approx}> 24. Star-galaxy separation poses a major challenge to such surveys because galaxies-even very compact galaxies-outnumber halo stars at these depths. We investigate photometric classification techniques on stars and galaxies with intrinsic FWHM <0.2 arcsec. We consider unsupervised spectral energy distribution template fitting and supervised, data-driven support vector machines (SVMs). For template fitting, we use a maximum likelihood (ML) method and a new hierarchical Bayesian (HB) method, which learns the prior distribution of template probabilities from the data. SVM requires training data to classify unknown sources; ML and HB do not. We consider (1) a best-case scenario (SVM{sub best}) where the training data are (unrealistically) a random sampling of the data in both signal-to-noise and demographics and (2) a more realistic scenario where training is done on higher signal-to-noise data (SVM{sub real}) at brighter apparent magnitudes. Testing with COSMOS ugriz data, we find that HB outperforms ML, delivering {approx}80% completeness, with purity of {approx}60%-90% for both stars and galaxies. We find that no algorithm delivers perfect performance and that studies of metal-poor main-sequence turnoff stars may be challenged by poor star-galaxy separation. Using the Receiver Operating Characteristic curve, we find a best-to-worst ranking of SVM{sub best}, HB, ML, and SVM{sub real}. We conclude, therefore, that a well-trained SVM will outperform template-fitting methods. However, a normally trained SVM performs worse. Thus, HB template fitting may prove to be the optimal classification method in future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1978

Korean coastal water depth/sediment and land cover mapping (1:25,000) by computer analysis of LANDSAT imagery

Computer analysis was applied to single date LANDSAT MSS imagery of a sample coastal area near Seoul, Korea equivalent to a 1:50,000 topographic map. Supervised image processing yielded a test classification map from this sample image containing 12 classes: 5 water depth/sediment classes, 2 shoreline/tidal classes, and 5 coastal land cover classes at a scale of 1:25,000 and with a training set accuracy of 76%. Unsupervised image classification was applied to a subportion of the site analyzed and produced classification maps comparable in results in a spatial sense. The results of this test indicated that it is feasible to produce such quantitative maps for detailed study of dynamic coastal processes given a LANDSAT image data base at sufficiently frequent time intervals.

Park, K. Y.↗

NASA NTRS1994

Neural network with dynamically adaptable neurons

This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.

Tawel, Raoul↗

DOE OSTI2024

Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder

Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.

36 MATERIALS SCIENCE↗

DOE OSTI2017

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

Active Learning↗

DOE OSTI2017

Machine learnt bond order potential to model metal–organic (Co–C) heterostructures

A fundamental understanding of the inter-relationships between structure, morphology, atomic scale dynamics, chemistry, and physical properties of mixed metallic-covalent systems is essential to design novel functional materials for applications in flexible nano-electronics, energy storage and catalysis. To achieve such knowledge, it is imperative to develop robust and computationally efficient atomistic models that describe atomic interactions accurately within a single framework. Here, we present a unified Tersoff–Brenner type bond order potential (BOP) for a Co–C system, trained against lattice parameters, cohesive energies, equation of state, and elastic constants of different crystalline phases of cobalt as well as orthorhombic Co2C derived from density functional theory (DFT) calculations. The independent BOP parameters are determined using a combination of supervised machine learning (genetic algorithms) and local minimization via the simplex method. Our newly developed BOP accurately describes the structural, thermodynamic, mechanical, and surface properties of both the elemental components as well as the carbide phases, in excellent accordance with DFT calculations and experiments. Using our machine-learnt BOP potential, we performed large-scale molecular dynamics simulations to investigate the effect of metal/carbon concentration on the structure and mechanical properties of porous architectures obtained via self-assembly of cobalt nanoparticles and fullerene molecules. Such porous structures have implications in flexible electronics, where materials with high electrical conductivity and low elastic stiffness are desired. Using unsupervised machine learning (clustering), we identify the pore structure, pore-distribution, and metallic conduction pathways in self-assembled structures at different C/Co ratios. We find that as the C/Co ratio increases, the connectivity between the Co nanoparticles becomes limited, likely resulting in low electrical conductivity; on the other hand, such C-rich hybrid structures are highly flexible (i.e., low stiffness). The BOP model developed in this work is a valuable tool to investigate atomic scale processes, structure–property relationships, and temperature/pressure response of Co–C systems, as well as design organic–inorganic hybrid structures with a desired set of properties.

Narayanan, Badri↗

DOE OSTI2019

RNA-seq latent featurizer using center loss cost function

Predictive modeling of patient tumor growth response to drug treatment is severely limited by a lack of experimental data for training. Combining experimental data from several studies is an attractive approach, but presents two problems: batch effects and limited data for individual drugs and cell lines. Batch effects are caused by systematic procedural differences among studies, which causes systematic experimental outcome differences. Directly using these experimental results as features for machine learning commonly causes problems when training on one study and testing on another. This severely limits a model's ability to perform well on new experiments. Even after combining studies, predicting outcomes on new patient tumors remains an open challenge. We propose a semi-supervised, autoencoder-based, machine learning procedure, which learns a smaller set of gene expression features that are robust to batch effects using background information on a cell line or tissue's tumor type. We implemented this reduced feature representation and show that the new feature space clusters strongly according to tumor type. This experiment is carried out across multiple studies: CCLE, CTRP, gCSI, GDSC, NCI60, and patient derived tumors. We hypothesize that using a batch effect resistant feature set across studies will improve prediction performance. Genome Data Commons (GDC) gene expression profiles for publicly available human tissues and cell lines from NCI60 and CCLE were processed using the semi-supervised learning procedure. Our autoencoder repurposes the 'center loss' (CL) cost function of Wen et. al. to learn a more generalized set of features using the cell line or tissue's tumor type.

He, Stewart↗

DOE OSTI2022

Predicting the oxidation states of Mn ions in the oxygen-evolving complex of photosystem II using supervised and unsupervised machine learning

Abstract Serial Femtosecond Crystallography at the X-ray Free Electron Laser (XFEL) sources enabled the imaging of the catalytic intermediates of the oxygen evolution reaction of Photosystem II (PSII). However, due to the incoherent transition of the S-states, the resolved structures are a convolution from different catalytic states. Here, we train Decision Tree Classifier and K-means clustering models on Mn compounds obtained from the Cambridge Crystallographic Database to predict the S-state of the X-ray, XFEL, and CryoEM structures by predicting the Mn’s oxidation states in the oxygen-evolving complex. The model agrees mostly with the XFEL structures in the dark S 1 state. However, significant discrepancies are observed for the excited XFEL states (S 2 , S 3, and S 0 ) and the dark states of the X-ray and CryoEM structures. Furthermore, there is a mismatch between the predicted S-states within the two monomers of the same dimer, mainly in the excited states. We validated our model against other metalloenzymes, the valence bond model and the Mn spin densities calculated using density functional theory for two of the mismatched predictions of PSII. The model suggests designing a more optimized sample delivery and illumiation systems are crucial to precisely resolve the geometry of the advanced S-states to overcome the noncoherent S-state transition. In addition, significant radiation damage is observed in X-ray and CryoEM structures, particularly at the dangler Mn center (Mn4). Our model represents a valuable tool for investigating the electronic structure of the catalytic metal cluster of PSII to understand the water splitting mechanism.

Plant Sciences↗

DOE OSTI2020

Graph interpolating activation improves both natural and robust accuracies in data-efficient deep learning

Improving the accuracy and robustness of deep neural nets (DNNs) and adapting them to small training data are primary tasks in deep learning (DL) research. In this paper, we replace the output activation function of DNNs, typically the data-agnostic softmax function, with a graph Laplacian-based high-dimensional interpolating function which, in the continuum limit, converges to the solution of a Laplace–Beltrami equation on a high-dimensional manifold. Furthermore, we propose end-to-end training and testing algorithms for this new architecture. The proposed DNN with graph interpolating activation integrates the advantages of both deep learning and manifold learning. Compared to the conventional DNNs with the softmax function as output activation, the new framework demonstrates the following major advantages: First, it is better applicable to data-efficient learning in which we train high capacity DNNs without using a large number of training data. Second, it remarkably improves both natural accuracy on the clean images and robust accuracy on the adversarial images crafted by both white-box and black-box adversarial attacks. Third, it is a natural choice for semi-supervised learning. This paper is a significant extension of our earlier work published in NeurIPS, 2018. For reproducibility, the code is available at https://github.com/BaoWangMath/DNN-DataDependentActivation .

Mathematics↗

DOE OSTI2022

Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks

ABSTRACT We compare the two largest galaxy morphology catalogues, which separate early- and late-type galaxies at intermediate redshift. The two catalogues were built by applying supervised deep learning (convolutional neural networks, CNNs) to the Dark Energy Survey data down to a magnitude limit of ∼21 mag. The methodologies used for the construction of the catalogues include differences such as the cutout sizes, the labels used for training, and the input to the CNN – monochromatic images versus gri-band normalized images. In addition, one catalogue is trained using bright galaxies observed with DES (i < 18), while the other is trained with bright galaxies (r < 17.5) and ‘emulated’ galaxies up to r-band magnitude 22.5. Despite the different approaches, the agreement between the two catalogues is excellent up to i < 19, demonstrating that CNN predictions are reliable for samples at least one magnitude fainter than the training sample limit. It also shows that morphological classifications based on monochromatic images are comparable to those based on gri-band images, at least in the bright regime. At fainter magnitudes, i > 19, the overall agreement is good (∼95 per cent), but is mostly driven by the large spiral fraction in the two catalogues. In contrast, the agreement within the elliptical population is not as good, especially at faint magnitudes. By studying the mismatched cases, we are able to identify lenticular galaxies (at least up to i < 19), which are difficult to distinguish using standard classification approaches. The synergy of both catalogues provides an unique opportunity to select a population of unusual galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

DOC-DICAM: Domain Aware One Class Defect Identification in Composite Aerostructure Material

Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.

anomaly detection↗

DOE OSTI2023

Knowledge Distillation for Anomaly Detection

Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model size. However, the size is often a limiting factor for the deployment on resource-constrained devices. Here, we present a novel procedure based on knowledge distillation for compressing an unsupervised anomaly detection model into a supervised deployable one and we suggest a set of techniques to improve the detection sensitivity. Compressed models perform comparably to their larger counterparts while significantly reducing the size and memory footprint.

Pol, Adrian Alan↗

DOE OSTI2015

Assessment of defense-related uranium mines in the United States and other US DOE office of legacy management domestic and international uranium initiatives - 15335

US DOE's Office of Legacy Management (LM) has taken several initiatives to improve domestic uranium mining and milling operations in the United States and to contribute to the management of legacy uranium mining and milling sites around the world. Initiatives include completing the 2014 Report to Congress on Defense-Related Uranium Mines, managing the Title X Uranium and Thorium Program with the Office of Environmental Management, and assisting IAEA with uranium issues in member state countries. The 2014 Report to Congress identified abandoned uranium mines (AUMs) in the United States that provided uranium ore to the US Atomic Energy Commission (AEC) between 1947 and 1970. The report identified AUM locations, reclamation/remediation status, radiological risks and other hazards, costs for reclamation and remediation, and potential prioritization schemes for mine cleanup. To develop reclamation and remediation cleanup cost estimates and assess risks, AUMs were grouped into six categories by tons of uranium ore produced, ranging from Small (less than 91 metric tons [100 tons]) to Very Large (greater than 453,900 metric tons [500,000 tons]). Radiological risk was calculated using the RESRAD computer code for five exposure scenarios: offsite resident, onsite resident, occasional visitor, recreational visitor, and mine reclamation worker. Results indicate that 69 percent of the AUMs are in Colorado and Utah and 23 percent are in Arizona, Wyoming, and New Mexico; and that the majority of the AUMs (68 percent) were considered Small or Small/Medium production mines, producing less than 908 metric tons (1,000 tons) of uranium ore. Although New Mexico has fewer AUMs (247) than the other states in the Colorado Plateau region, AUMs in that state (primarily those in the Grants Mineral Belt) produced 45 percent of the 79.5 million tons of AEC-purchased uranium ore. About 50 percent of the AUMs are on public land managed by the US Bureau of Land Management; 435 AUMs are on the Navajo Nation. Risk estimates for the onsite resident scenario (plausible on tribal and non-federal land) could result in an incremental cancer risk greater than 10{sup -4}. Radon inhalation was the dominant contributor to radiological risk for the five exposure scenarios evaluated. Different state and federal agencies are conducting cleanup of some AUMs under various remedial and legal authorities. Activities constituting mine 'reclamation' may reduce radiological risks to humans to acceptable levels for many mines on federal public lands if the occasional visitor and recreational visitor exposure scenarios are assumed. Addressing physical hazards (e.g., open shafts) at mines where conditions could cause serious injuries is typically a priority of public land management agencies. At locations where mine-related groundwater contamination occurs, it is a significant contributor to cleanup costs. However, most Small and Small/Medium AUMs were likely developed above the water table, and some wet mines are located in areas where groundwater has high, naturally occurring levels of the same constituents (including radioactive elements) that are typically associated with uranium mines. LM is also administering, with the US DOE Office of Environmental Management, the Title X program, which was developed by Congress to reimburse private companies (i.e., those that held active operating licenses prior to 1978) for a portion of their reclamation costs for mill sites that processed ore sold to AEC. LM recently updated the program guidance document and established internal protocols for annually auditing companies. LM is assisting IAEA on two different initiatives: assisting developing countries with training on the basic steps to take to perform reclamation of uranium mines, and developing technical guidance and case studies related to safety and environmental assessments and post-closure management of uranium legacy sites as part of the IAEA Regulatory Supervision of Legacy Sites initiative. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2014

Automatic classification of time-variable X-ray sources

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

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

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Generative Machine Learning↗

DOE OSTI2022

A differentiable approach to the maximum independent set problem using dataless neural networks

The success of machine learning solutions for reasoning about discrete structures has brought attention to its adoption within combinatorial optimization algorithms. Such approaches generally rely on supervised learning by leveraging datasets of the combinatorial structures of interest drawn from some distribution of problem instances. Reinforcement learning has also been employed to find such structures. Here, in this paper, we propose a different approach in that no data is required for training the neural networks that produce the solution. In this sense, what we present is not a machine learning solution, but rather one that is dependent on neural networks and where backpropagation is applied to a loss function defined by the structure of the neural network architecture as opposed to a training dataset. In particular, we reduce the popular combinatorial optimization problem of finding a maximum independent set to a neural network and employ a dataless training scheme to refine the parameters of the network such that those parameters yield the structure of interest. Additionally, we propose a universal graph reduction procedure to handle large-scale graphs. The reduction exploits community detection for graph partitioning and is applicable to any graph type and/or density. Experimental results on both real and synthetic graphs demonstrate that our proposed method performs on par or outperforms state-of-the-art learning-based methods in terms of the size of the found set without requiring any training data.

97 MATHEMATICS AND COMPUTING↗