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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
DOE OSTI2021

Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation

We report the Boltzmann equation with the Bhatnagar-Gross-Krook collision model (Boltzmann-BGK equation) has been employed to describe multiscale flows, i.e., from the hydrodynamic limit to free molecular flow. In this study we employ physics-informed neural networks (PINNs) to solve forward and inverse problems via the Boltzmann-BGK formulation (PINN-BGK), enabling PINNs to model flows in both the continuum and rarefied regimes. In particular, the PINN-BGK is composed of three sub-networks, i.e., the first for approximating the equilibrium distribution function, the second for approximating the non-equilibrium distribution function, and the third one for encoding the Boltzmann-BGK equation as well as the corresponding boundary/initial conditions. By minimizing the residuals of the governing equations and the mismatch between the predicted and provided boundary/initial conditions, we can approximate the Boltzmann-BGK equation for both continuous and rarefied flows. For forward problems, the PINN-BGK is utilized to solve various benchmark flows given boundary/initial conditions, e.g. Kovasznay flow, Taylor-Green flow, cavity flow, and micro Couette flow for Knudsen number up to 5. For inverse problems, we focus on rarefied flows in which accurate boundary conditions are difficult to obtain. We employ the PINN-BGK to infer the flow field in the entire computational domain given a limited number of interior scattered measurements on the velocity without using the (unknown) boundary conditions. Results for the two-dimensional micro Couette and micro cavity flows with Knudsen numbers ranging from 0.1 to 10 indicate that the PINN-BGK can infer the velocity field in the entire domain with good accuracy. Finally, we also present some results on using transfer learning to accelerate the training process. Specifically, we can obtain a three-fold speedup comparted to the standard training process (e.g., Adam plus L-BFG-B) for the two-dimensional flow problems considered in our work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Deep-learning-aided extraction of optical constants in scanning near-field optical microscopy

Scanning near-field optical microscopy is one of the most effective techniques for spectroscopy of nanoscale systems. However, inferring optical constants from the measured near-field signal can be challenging because of a complicated and highly nonlinear interaction between the scanned probe and the sample. Conventional fitting methods applied to this problem often suffer from the lack of convergence or require human intervention. Here, we develop an alternative approach where the optical parameter extraction is automated by a deep learning network. The network provides an initial estimate that is subsequently refined by a traditional fitting algorithm. In conclusion, we show that this method demonstrates superior accuracy, stability against noise, and computational speed when applied to simulated near-field spectra.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A Weakly-Supervised, Multitask Deep Learning Framework for Shadow Mitigation in Remote Sensing Imagery

We propose a weakly-supervised, multitask framework for training a convolutional neural network to solve the problem of cloud shadow mitigation given only cloud and shadow masks as labels. The network minimizes the Wasserstein distance between shadows and their proximal sunlit neighborhoods, generating a supervisory signal directly from within the input image. We extract further utility from the shadow mask through multitask learning by introducing an auxiliary task of shadow segmentation. Our approach is advantageous since it performs mitigation in an end-to-end framework which requires only a shadowed image for inference. We apply this process to the Landsat 8 OLI SPARCS validation data set and demonstrate plausible results.

Couwenhoven, Scott↗
DOE OSTI2020

Sensing Service Transformer Secondary Currents using Planar Magnetic Pick-up Coils

The rapid deployment of distributed energy resources (DERs) is creating stresses on utility assets in the distribution networks. The service transformer, which is the most commonly used utility asset is often not monitored, since the cost of available sensing solutions is as high as the transformer itself. This paper presents a method to use an array of printed circuit board coils to intelligently monitor North American pole top service transformers. The approach, uses a novel, self-calibrating algorithm to infer the fixed geometry of the installation, by recording data over an extended period of time. The proposed method can enable rapid installation of a non-intrusive sensor that can be used to monitor loading levels of the distribution transformer. Furthermore, these sensors can help in planning, prognostics and advanced situational awareness for utilities.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2004

More Far-Side Deep Moonquake Nests Discovered

As reported last year, we started to reanalyze the seismic data acquired from 1969 to 1977 with a network of stations established on the Moon during the Apollo mission. The reason for the reanalysis was because recent advances in computer technology make it possible to employ much more sophisticated analysis techniques than was possible previously. The primary objective of the reanalysis was to search for deep moonquakes on the far side of the Moon and, if found, to use them to infer the structure of the Moon's deep interior, including a possible central core. The first step was to identify any new deep moonquakes that escaped our earlier search by applying a combination of waveform cross-correlation and single-link cluster analysis, and then to see if any of them are from previously unknown nests of deep moonquakes. We positively identified 7245 deep moonquakes, more than a five-fold increase from the previous 1360. We also found at least 88 previously unknown deep-moonquake nests. The question was whether any of these newly discovered nets were on the far side of the Moon, and we now report that our analysis of the data indicates that some of them are indeed on the far side.

Nakamura, Y.↗
NASA NTRS1998

Nuclear Instruments and Methods in Physics Research: Microstructural Characterization of Semi-Interpenetrating Polymer Networks by Positron Lifetime Spectroscopy - Section B

Thermoset and thermoplastic polyimides have complementary physical/mechanical properties. Whereas thermoset polyimides are brittle and generally easier to process, thermoplastic polyimides are tough but harder to process. It is expected that a combination of these two types of polyimides may help produce polymers more suitable for aerospace applications. Semi-Interpenetrating Polymer Networks (S-IPNs) of thermoset LaRC(Trademark)-RP46 and thermoplastic LARC(Trademark)-IA polyimides were prepared in weight percent ratios ranging from 100:0 to 0: 100. Positron lifetime measurements were made in these samples to correlate their free volume features with physical/mechanical properties. As expected, positronium atoms are not formed in these samples. The second life time component has been used to infer the positron trap dimensions. The "free volume" goes through a minimum at about 50:50 ratio, suggesting that S-IPN samples are not merely solid solutions of the two polymers. These data and related structural properties of the S-IPN samples have been discussed in this paper.

Singh, Jag J.↗
NASA NTRS2019

NASA SEXTANT Mission Operations Architecture

The Station Explorer for X-Ray Timing and Navigation (SEXTANT) mission is a technology demonstration enhancement to the Neutron Star Interior Composition Explorer (NICER) mission, a NASA Astrophysics Explorer Mission of Opportunity to the International Space Station (ISS) that was launched in June of 2017. The NICER instrument is a precision pointing X-ray telescope that times pulsar sourced photon arrivals which the SEXTANT mission uses to perform autonomous onboard X-ray Pulsar Navigation (XNAV). By comparing the detected time of arrival of X-ray photons to a reference of expected pulsar timing models, one can infer a range and range rate measurement based on light time delay. Since both timing and orientation information comes from a celestial source, this technology could provide a GPS-like navigation capability available throughout our Solar System and beyond. Applications that XNAV can support include outer planet and interstellar missions, manned missions, libration orbit missions, and current infrastructure such as the Deep Space Network (DSN). The SEXTANT team successfully completed a first demonstration of in-space and autonomous XNAV in November 2017. NICER and SEXTANT have separate teams, with NICER being the primary team with its own science objectives. Operational modes for both missions must have concurrent and independent components as well as an integrated ground system. This paper describes on the operational infrastructure and successful operations for performing X-ray Pulsar Navigation with the Station Explorer for X-ray Timing and Navigation Technology (SEXTANT) demonstration. This paper first details the infrastructure implemented, then the concept of operations, and finally the operations for the SEXTANT demonstration and lessons learned.

Ground Systems↗
NASA NTRS2020

GW190521: A Binary Black Hole Merger with a Total Mass of 150 Mꙩ

On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of 85 (+21,−14) Mꙩ and 66 (+17,−18) Mꙩ (90% credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65 M⊙. We calculate the mass of the remnant to be 142 (+28,−16) Mꙩ, which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is 5.3 (+2.4,−2.6) Gpc, corresponding to a redshift of 0.82 (+0.28,−0.34) . The inferred rate of mergers similar to GW190521 is 0.13 (+0.30,−0.11) Gpc^(−3) per yr.

R. Abbott↗
DOE OSTI2021

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Scaling kinetic Monte-Carlo simulations of grain growth with combined convolutional and graph neural networks

Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we suggest a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160 3 ), our method reduces memory usage and runtime in inference by 117× and 115×, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder’s ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. Training data are generated from stochastic grain growth simulations, providing realistic variability for learning robust microstructure evolution. Comprehensive system validation confirms that the model is accurate, robust, and scalable.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Adjustable impedance, force feedback and command language aids for telerobotics (parts 1-4 of an 8-part MIT progress report)

Projects recently completed or in progress at MIT Man-Machine Systems Laboratory are summarized. (1) A 2-part impedance network model of a single degree of freedom remote manipulation system is presented in which a human operator at the master port interacts with a task object at the slave port in a remote location is presented. (2) The extension of the predictor concept to include force feedback and dynamic modeling of the manipulator and the environment is addressed. (3) A system was constructed to infer intent from the operator's commands and the teleoperation context, and generalize this information to interpret future commands. (4) A command language system is being designed that is robust, easy to learn, and has more natural man-machine communication. A general telerobot problem selected as an important command language context is finding a collision-free path for a robot.

Sheridan, Thomas B.↗
NASA NTRS1989

Optical pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

Papers on optical pattern recognition are presented, covering topics such as the estimation of satellite pose and motion parameters using a neural net tracker, associative memory, optical implmentation of programmable neural networks, optoelectronic neural networks, dynamic autoassociative neural memory, heteroassociative memory, bilinear pattern recognition processors, optical processing of optical correlation plane data, and a synthetic discriminant function-based nonlinear optical correlator. Other topics include an interactive optical-digital image processor, geometric transformations for video compression and human teleoperator display, quasiconformal remapping for compensation of human visual field defects, hybrid vision for automated spacecraft landing, advanced symbolic and inference optical correlation filters, and a rotationally invariant holographic tracking system. Additional topics include the detection of rotational and scale-varying objects with a programmable joint transform correlator, a single spatial light modulator binary nonlinear optical correlator, optical joint transform correlation, linear phase coefficient composite filters, and binary phase-only filters.

Liu, Hua-Kuang↗
NASA NTRS1996

Microstructural Characterization of Semi-Interpenetrating Polymer Networks by Positron Lifetime Spectroscopy

Thermoset and thermoplastic polyimides have complementary physical and mechanical properties. Whereas thermoset polyimides are brittle and generally easier to process, thermoplastic polyimides are tough but harder to process. A combination of these two types of polyimides may help produce polymers more suitable for aerospace applications. Semi-Interpenetrating Polymer Networks (S-IPN) of thermoset LaRC(TM)-RP46 and thermoplastic LaRC(TM)-IA polyimides were prepared in weight percent ratios ranging from 100:0 to 0:100. Positron lifetime measurements were made in these samples to correlate their free volume features with physical and mechanical properties. As expected, positronium atoms are not formed in these samples. The second lifetime component has been used to infer the positron trap dimensions. The 'free volume' goes through a minimum at a ratio of about 50:50, and this suggests that S-IPN samples are not merely solid solutions of the two polymers. These data and related structural properties of the S-IPN samples are discussed.

Singh, Jag J.↗
NASA NTRS1990

CLIPS: A tool for corn disease diagnostic system and an aid to neural network for automated knowledge acquisition

This paper describes the building of a corn disease diagnostic expert system using CLIPS, and the development of a neural expert system using the fact representation method of CLIPS for automated knowledge acquisition. The CLIPS corn expert system diagnoses 21 diseases from 52 symptoms and signs with certainty factors. CLIPS has several unique features. It allows the facts in rules to be broken down to object-attribute-value (OAV) triples, allows rule-grouping, and fires rules based on pattern-matching. These features combined with the chained inference engine result to a natural user query system and speedy execution. In order to develop a method for automated knowledge acquisition, an Artificial Neural Expert System (ANES) is developed by a direct mapping from the CLIPS system. The ANES corn expert system uses the same OAV triples in the CLIPS system for its facts. The LHS and RHS facts of the CLIPS rules are mapped into the input and output layers of the ANES, respectively; and the inference engine of the rules is imbedded in the hidden layer. The fact representation by OAC triples gives a natural grouping of the rules. These features allow the ANES system to automate rule-generation, and make it efficient to execute and easy to expand for a large and complex domain.

Wu, Cathy↗
DOE OSTI2020

GW190521: A Binary Black Hole Merger with a Total Mass of 150 M ⊙

On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of 8 5 - 14 + 21 M ⊙ and 6 6 - 18 + 17 M ⊙ (90% credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65 M ⊙ . We calculate the mass of the remnant to be 14 2 - 16 + 28 M ⊙ , which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is 5.3 - 2.6 + 2.4 Gpc , corresponding to a redshift of 0.82 - 0.34 + 0.28 . The inferred rate of mergers similar to GW190521 is 0.13 - 0.11 + 0.30 Gpc - 3 yr - 1 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

Here we present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.

42 ENGINEERING↗
DOE OSTI2025

Inverse model based error detection in beamline optics

Optics tuning in transfer lines and LINACs can be challenging due to the fact that multiple combinations of machine settings can lead to the same diagnostic output. Moreover, the lack of a periodic solution can limit the ability to infer optics in the same way as rings from BPM signals. Model based approaches are often used to assist with the optics tuning in combination with optimization or parameter estimation. Here we have developed a novel approach using machine learning inverse models trained on a known configuration to detect variations in quadrupole settings without explicitly including them in the model. This paper shows a comparison of neural network models and linear models on both a simulation based study and experimental studies conducted at the AGS to RHIC transfer line at Brookhaven National Lab.

43 PARTICLE ACCELERATORS↗