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At least 973 records · Page 54

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Robust deep learning framework for constitutive relations modeling

Modeling the full-range deformation behaviors of materials under complex loading and materials conditions is a significant challenge for constitutive relations (CRs) modeling. Here, we propose a general encoder-decoder deep learning framework that can model high-dimensional stress-strain data and complex loading histories with robustness and universal capability. The framework employs an encoder to project high-dimensional input information (e.g., loading history, loading conditions, and materials information) to a lower-dimensional hidden space and a decoder to map the hidden representation to the stress of interest. We evaluated various encoder architectures, including gated recurrent unit (GRU), GRU with attention, temporal convolutional network (TCN), and the Transformer encoder, on two complex stress-strain datasets that were designed to include a wide range of complex loading histories and loading conditions. All architectures achieved excellent test results with an root-mean-square error (RMSE) below 1 MPa. Additionally, we analyzed the capability of the different architectures to make predictions on out-of-domain applications, with an uncertainty estimation based on deep ensembles. The proposed approach provides a robust alternative to empirical/semi-empirical models for CRs modeling, offering the potential for more accurate and efficient materials design and optimization.

36 MATERIALS SCIENCE↗

NASA NTRS2004

Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning Observations

Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.

Boccippio, Dennis↗

NASA NTRS1994

Empirical modeling for intelligent, real-time manufacture control

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.

Xu, Xiaoshu↗

NASA NTRS2003

Long-range planning for the Deep Space Network

Conduct of space exploration is undergoing a significant transformation. Initial reconnaissance missions are giving way to long duration observations with data-intensive instruments, in situ investigations and complex operations. To keep pace, a transformation in the Deep Space Network is in order.

DSN long range plan↗

NASA NTRS1992

Application of space-time neural networks to detect tether skiprope phenomenon in space operations

The feasibility of operating tethered payloads in earth orbit will be studied during a space shuttle flight scheduled for 1992. Tethered systems may exhibit a circular transverse oscillation or skiprope phenomenon due to interaction between the earth's magnetic field and current pulsing through the tether. Effective damping of this skiprope motion depends on rapid and accurate detection of its magnitude and phase. Satellite attitude motion has characteristic oscillations as well as many other perturbations and therefore the relationship between skiprope parameters and attitude time history is very complex and nonlinear. A space-time neural network (STNN) for filtering satellite rate gyro data is proposed for rapid detection and prediction of skiprope magnitude and phase. A validated orbital operations simulator and STNN software will be used for training and testing of this skiprope detection system. The advantages of STNNs are discussed and STNN configurations and preliminary results are presented.

Lea, Robert N.↗

DOE OSTI2022

Graph identification of proteins in tomograms ( GRIP‐Tomo )

Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1989

Measurements of complex permittivity of microwave substrates in the 20 to 300 K temperature range from 26.5 to 40.0 GHz

A knowledge of the dielectric properties of microwave substrates at low temperatures is useful in the design of superconducting microwave circuits. Results are reported for a study of the complex permittivity of sapphire (Al2O3), magnesium oxide (MgO), silicon oxide (SiO2), lanthanum aluminate (LaAlO3), and zirconium oxide (ZrO2), in the 20 to 300 Kelvin temperature range, at frequencies from 26.5 to 40.0 GHz. The values of the real and imaginary parts of the complex permittivity were obtained from the scattering parameters, which were measured using a HP-8510 automatic network analyzer. For these measurements, the samples were mounted on the cold head of a helium gas closed cycle refrigerator, in a specially designed vacuum chamber. An arrangement of wave guides, with mica windows, was used to connect the cooling system to the network analyzer. A decrease in the value of the real part of the complex permittivity of these substrates, with decreasing temperature, was observed. For MgO and Al2O3, the decrease from room temperature to 20 K was of 7 and 15 percent, respectively. For LaAlO3, it decreased by 14 percent, for ZrO2 by 15 percent, and for SiO2 by 2 percent, in the above mentioned temperature range.

Miranda, Felix A.↗

NASA NTRS1990

Measurements of complex permittivity of microwave substrates in the 20 to 300 K temperature range from 26.5 to 40.0 GHz

A knowledge of the dielectric properties of microwve substrates at low temperatures is useful in the design of superconducting microwave circuits. Results are reported for a study of the complex permittivity of sapphire (Al2O3), magnesium oxide (MgO), silicon oxide (SiO2), lanthanum aluminate (LaAlO3), and zirconium oxide (ZrO2), in the 20 to 300 Kelvin temperature range, at frequencies from 26.5 to 40.0 GHz. The values of the real and imaginary parts of the complex permittivity were obtained from the scattering parameters, which were measured using an HP-8510 automatic network analyzer. For these measurements, the samples were mounted on the cold head of a helium gas closed cycle refrigerator, in a specially designated vacuum chamber. An arrangement of wave guides, with mica windows, was used to connect the cooling system to the network analyzer. A decrease in the value of the real part of the complex permittivity of these substrates, with decreasing temperature, was observed. For MgO and Al2O3, the decrease from room temperature to 20 K was of 7 and 15 percent, respectively. For LaAlO3, it decreased by 14 percent, for ZrO2 by 15 percent, and for SiO2 by 2 percent, in the above mentioned temperature range.

Miranda, Felix A.↗

NASA NTRS1986

Queueing Network Models for Parallel Processing of Task Systems: an Operational Approach

Computer performance modeling of possibly complex computations running on highly concurrent systems is considered. Earlier works in this area either dealt with a very simple program structure or resulted in methods with exponential complexity. An efficient procedure is developed to compute the performance measures for series-parallel-reducible task systems using queueing network models. The procedure is based on the concept of hierarchical decomposition and a new operational approach. Numerical results for three test cases are presented and compared to those of simulations.

Mak, Victor W. K.↗

DOE OSTI2022

Enabling Innovation in Wind Turbine Design Using Artificial Intelligence

The Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements (INTEGRATE) project is developing a new inverse-design capability for wind turbine rotors using invertible neural networks. This artificial intelligence (AI)-based technology can capture complex nonlinear aerodynamic effects 100 times faster than alternative design approaches.

aerodynamics↗

NASA NTRS1994

A feasibility study for long-path multiple detection using a neural network

Least-squares inverse filters have found widespread use in the deconvolution of seismograms and the removal of multiples. The use of least-squares prediction filters with prediction distances greater than unity leads to the method of predictive deconvolution which can be used for the removal of long path multiples. The predictive technique allows one to control the length of the desired output wavelet by control of the predictive distance, and hence to specify the desired degree of resolution. Events which are periodic within given repetition ranges can be attenuated selectively. The method is thus effective in the suppression of rather complex reverberation patterns. A back propagation(BP) neural network is constructed to perform the detection of first arrivals of the multiples and therefore aid in the more accurate determination of the predictive distance of the multiples. The neural detector is applied to synthetic reflection coefficients and synthetic seismic traces. The processing results show that the neural detector is accurate and should lead to an automated fast method for determining predictive distances across vast amounts of data such as seismic field records. The neural network system used in this study was the NASA Software Technology Branch's NETS system.

Feuerbacher, G. A.↗

DOE OSTI2022

ITeM: Independent temporal motifs to summarize and compare temporal networks

We report networks are a fundamental and flexible way of representing various complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Temporal networks are a specialization of general networks where every relationship occurs at a discrete time. The temporal evolution of such networks is as important to understand as the structure of the entities and relationships. We present the Independent Temporal Motif (ITeM) to characterize temporal graphs from different domains. ITeMs can be used to model the structure and the evolution of the graph. In contrast to existing work, ITeMs are edge-disjoint directed motifs that measure the temporal evolution of ordered edges within the motif. For a given temporal graph, we produce a feature vector of ITeM frequencies and the time it takes to form the ITeM instances. We apply this distribution to measure the similarity of temporal graphs. We show that ITeM has higher accuracy than other motif frequency-based approaches. We define various ITeM-based metrics that reveal salient properties of a temporal network. We also present importance sampling as a method to efficiently estimate the ITeM counts. We present a distributed implementation of the ITeM discovery algorithm using Apache Spark and GraphFrame. We evaluate our approach on both synthetic and real temporal networks.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

INSPiRE – An Approach to Mission Quality Management using Network Slicing for Space Applications

Managing traffic between the Earth-Moon and Earth-Mars is a complex process requiring significant investment in resources and expertise at NASA. INSPiRE improves the performance of space networks by enabling a dynamic re-configuration process that works for any mixed topology over a heterogeneous and multi-vendor network. To achieve the desired functionality, INSPiRE incorporates a set of algorithms, machine learning processes, and policy inference to handle unpredictable, disruptive events. INSPiRE draws parallels from the current notion of the 3GPP (5G and beyond) Network Slicing approach, where the same physical network divides into several virtual networks, and for each of these virtual networks, there is a guaranteed Quality of Service for the missions that they serve.

cognitive communications↗

DOE OSTI2020

Amino acid interacting network in the receptor-binding domain of SARS-CoV-2 spike protein

The relation between amino acid (AA) sequence and biologically active conformation controls the process of polypeptide chains folding into three-dimensional (3d) protein structures. The recent achievements in the resolution achieved in cryo-electron microscopy coupled with improvements in computational methodologies have accelerated the analysis of structures and properties of proteins. However, the detailed interaction between AAs has not been fully elucidated. Herein, we present a de novo method to evaluate inter-amino acid interactions based on the concept of accurately evaluating the amino acid bond pairs (AABP). The results obtained enabled the identification of complex 3d long-range interconnected AA interacting network in proteins. The method is applied to the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. We show that although nearest-neighbor AAs in the primary sequence have large AABP, other nonlocal AAs make substantial contribution to AABP with significant participation of both covalent and hydrogen bonding. Detailed analysis of AABP in RBD reveals the pivotal role they play in sequence conservation with profound implications on residue mutations and for therapeutic drug design. This approach could be easily applied to many other proteins of biomedical interest in life sciences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Insights on the dissolution of water in an albite melt at high pressures and temperatures from a direct structural analysis

The water dissolution mechanism in silicate melts under high pressures is not well understood. Here we present the first direct structure investigation of a water-saturated albite melt to monitor the interactions between water and the network structure of silicate melt at the molecular level. In situ high-energy X-ray diffraction was carried out on the NaAlSi 3 O 8 -H 2 O system at 800 °C and 300 MPa, at the Advanced Photon Source synchrotron facility. The analysis of the X-ray diffraction data was augmented with classical Molecular Dynamics simulations of a hydrous albite melt, incorporating accurate water-based interactions. The results show that metal–oxygen bond breaking at the bridging sites occurs overwhelmingly at the Si site upon reaction with H 2 O, with subsequent Si–OH bond formation and negligible Al–OH formation. Furthermore, we see no evidence for the dissociation of the Al 3+ ion from the network structure upon breaking of the Si–O bond in the hydrous albite melt. The results also indicate that the Na + ion is an active participant in the modifications of the silicate network structure of the albite melt upon water dissolution at high P–T conditions. We do not find evidence for the Na + ion dissociating from the network structure upon depolymerization and subsequent formation of NaOH complexes. Instead, our results show that the Na + ion persists as a structure modifier with a shift away from Na–BO bonding to an increase in the extent of Na-NBO bonding, in parallel with pronounced depolymerization of the network. Our MD simulations show that the Si–O and Al–O bond lengths are expanded by about 6% in the hydrous albite melt compared to those of the dry melt at high P–T conditions. The changes in the network silicate structure of a hydrous albite melt at high pressure and temperature, as revealed in this study, must be considered in the advancement of water dissolution models of hydrous granitic (or alkali aluminosilicate) melts.

36 MATERIALS SCIENCE↗

NASA NTRS2006

Technology developments integrating a space network communications testbed

hybrid simulations↗

NASA NTRS2006

Technology Developments Integrating a Space Network Communications Testbed

As future manned and robotic space explorations missions involve more complex systems, it is essential to verify, validate, and optimize such systems through simulation and emulation in a low cost testbed environment. The goal of such a testbed is to perform detailed testing of advanced space and ground communications networks, technologies, and client applications that are essential for future space exploration missions. We describe the development of new technologies enhancing our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) that enable its integration in a distributed space communications testbed. MACHETE combines orbital modeling, link analysis, and protocol and service modeling to quantify system performance based on comprehensive considerations of different aspects of space missions. It can simulate entire networks and can interface with external (testbed) systems. The key technology developments enabling the integration of MACHETE into a distributed testbed are the Monitor and Control module and the QualNet IP Network Emulator module. Specifically, the Monitor and Control module establishes a standard interface mechanism to centralize the management of each testbed component. The QualNet IP Network Emulator module allows externally generated network traffic to be passed through MACHETE to experience simulated network behaviors such as propagation delay, data loss, orbital effects and other communications characteristics, including entire network behaviors. We report a successful integration of MACHETE with a space communication testbed modeling a lunar exploration scenario. This document is the viewgraph slides of the presentation.

hybrid simulation↗

DOE OSTI2025

SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver

Abstract Solving large-scale inverse problems using deep-learning algorithms have become an essential part of modern research and industrial applications. The complexity of the underlying inverse problem may require the utilization of high performance computing systems which poses a challenge on the algorithmic design of the inverse problem solver. Most deep learning algorithms require, due to their design, custom parallelization techniques in order to be resource efficient while showing a reasonable convergence. In this paper we introduce a S calable A synchronous G enerative I nverse P roblem S olver (SAGIPS) on high-performance computing systems. We present a workflow that utilizes an asynchronous ring-allreduce algorithm to transfer the gradients of the generator network across multiple GPUs. Experiments with a scientific proxy application demonstrate that SAGIPS shows near linear weak scaling, together with a convergence quality that is comparable to traditional methods. The approach presented here allows leveraging Generative Adverserial Network across multiple GPUs, promising advancements in solving complex inverse problems at scale.

97 MATHEMATICS AND COMPUTING↗