Search NASA⌕ Search

SEARCH · Search NASA

Results for “COMPUTING NETWORK”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,099 records · Page 61

Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning

Materials processing often occurs under extreme dynamic conditions leading to a multitude of unique structural environments. These structural environments generally occur at high temperatures and/or high pressures, often under non-equilibrium conditions, which results in drastic changes in the material's structure over time. Computational techniques, such as molecular dynamics simulations, can probe the atomic regime under these extreme conditions. However, characterizing the resulting diverse atomistic structures as a material undergoes extreme changes in its structure has proved challenging due to the inherently non-linear relationship between structures as large-scale changes occur. Here, we introduce SODAS++, a universal graph neural network framework, that can accurately and intuitively quantify the atomistic structural evolution corresponding to the transition between any two arbitrary phases. We showcase SODAS++ for both solid–solid and solid–liquid transitions for systems of increasing geometric and chemical complexity, such as colloidal systems, elemental Al, rutile and amorphous TiO 2 , and the non-stoichiometric ternary alloy Ag 26 Au 5 Cu 19 . Finally, we show that SODAS++ can accurately quantify all transitions in a physically interpretable manner, showcasing the power of unsupervised graph neural network encodings for capturing the complex and non-linear pathway, a material's structure takes as it evolves.

36 MATERIALS SCIENCE↗

Trainability of Dissipative Perceptron-Based Quantum Neural Networks

Several architectures have been proposed for quantum neural networks (QNNs), with the goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling results are urgently needed for specific QNN constructions to understand which, if any, will be trainable at a large scale. Here, we analyze the gradient scaling (and hence the trainability) for a recently proposed architecture that we call dissipative QNNs (DQNNs), where the input qubits of each layer are discarded at the layer’s output. We find that DQNNs can exhibit barren plateaus, i.e., gradients that vanish exponentially in the number of qubits. Moreover, we provide quantitative bounds on the scaling of the gradient for DQNNs under different conditions, such as different cost functions and circuit depths, and show that trainability is not always guaranteed. Here our work represents the first rigorous analysis of the scalability of a perceptron-based QNN.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Machine-learning approach to the design of OSDAs for zeolite beta

We report a machine-learning strategy for design of organic structure directing agents (OSDAs) for zeolite beta. We use machine learning to replace a computationally expensive molecular dynamics evaluation of the stabilization energy of the OSDA inside zeolite beta with a neural network prediction. We train the neural network on 4,781 candidate OSDAs, spanning a range of stabilization energies. We find that the stabilization energies predicted by the neural network are highly correlated with the molecular dynamics computations. We further find that the evolutionary design algorithm samples the space of chemically feasible OSDAs thoroughly. In total, we find 469 OSDAs with verified stabilization energies below -17 kJ/(mol Si), comparable to or better than known OSDAs for zeolite beta, and greatly expanding our previous list of 152 such predicted OSDAs. We expect that these OSDAs will lead to syntheses of zeolite beta.

42 ENGINEERING↗

Evolution of Robustness in Growing Random Networks

Networks are widely used to model the interaction between individual dynamic systems. In many instances, the total number of units and interaction coupling are not fixed in time, and instead constantly evolve. In networks, this means that the number of nodes and edges both change over time. Various properties of coupled dynamic systems, such as their robustness against noise, essentially depend on the structure of the interaction network. Therefore, it is of considerable interest to predict how these properties are affected when the network grows as well as their relationship to the growth mechanism. Here, we focus on the time evolution of a network’s Kirchhoff index. We derive closed-form expressions for its variation in various scenarios, including the addition of both edges and nodes. For the latter case, we investigate the evolution where single nodes with one or two edges connecting to existing nodes are added recursively to a network. In both cases, we derive the relations between the properties of the nodes to which the new node connects along with the global evolution of network robustness. In particular, we show how different scalings of the Kirchhoff index can be obtained as a function of the number of nodes. We illustrate and confirm this theory via numerical simulations of randomly growing networks.

97 MATHEMATICS AND COMPUTING↗

Development of a Filtered CFD-DEM Drag Model with Multiscale Markers Using an Artificial Neural Network and Nonlinear Regression

Here, the accuracy of coarse-grained Euler-Lagrangian simulations of fluidized beds heavily depends on the mesoscale drag models to account for the influences of the unresolved sub-grid structures. Traditional filtered drag models are regressed with mesoscale markers such as voidage and slip velocities. In this research, a filtered drag was regressed with both mesoscale and macro-scale markers using fine grid Computational Fluid Dynamics - Discrete Element Method (CFD-DEM) simulations. The traditional non-linear regression method was compared with machine learning regression using an Artificial Neural Network (ANN) implemented in PyTorch and coupled with MFiX. The new drag showed higher accuracy than the Wen-Yu drag and another filtered drag derived from the two-fluid model. The nonlinear regression shows slightly better results than ANN regression in cases with similar R 2 values. The utilization of the gas inlet velocity as an additional macro-scale marker reduced the errors by up to 55.3% in the tested cases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Light-powered end-to-end neutron detection and imaging with an edge-deployed optical AI chip

Neutron detection is widely used in many applications including nuclear physics, nuclear energy, nuclear technologies and nuclear safeguards. Developing an end-to-end neutron detection and imaging workflow paves way towards fully automated processes for many applications. We implemented an automated workflow for neutron detection experiments which use a solid state image sensor to capture neutron hits as a digital image. We deploy the workflow to an edge-based optical neural network (ONN) to increase the radiation-hardness and lifetime of neutron detection instruments. We present a two-stage neural network framework for detection of neutrons at sub-pixel resolution. The first stage uses a region proposal network to efficiently detect and extract neutron hits from the input camera image. The second stage feeds the extracted hits into a fully connected neural network to predict the sub-pixel hit position. The performance of the two-stage framework is evaluated using the edge-based ONN. The results show that we can achieve above 96% neutron detection accuracy as well as sub-pixel and sub-micron position resolution, while enjoying the advantages of the ONN hardware including radiation-hardness, low energy consumption and high computing speed for integrated edge camera and hardware deployment, when compared with electronic counterparts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

On the rejection-based algorithm for simulation and analysis of large-scale reaction networks

Stochastic simulation for in silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An Approach for Assessing Consequences of Potential Supply Chain and Insider Contributed Cyber Attacks on Nuclear Power Plants

The Stuxnet attack at the Natanz facility is an example of a targeted and successful cyber attack on a nuclear facility. Snowden's release of National Security Agency documents demonstrated the consequences of the insider threat. More recently, the United States tried to attack North Korea but failed, South Korea was attempting to attack North Korea, and both applied Stuxnet-like approaches. These sophisticated targeted attacks differ from web-site hacking events that are reported almost daily in the news mainly because targeted attacks require detailed design and operation information of the systems attacked and/or are often carried out by insiders. For instance, in order to minimize disruption of facilities around the world, Stuxnet remained idle until it recognized the specific configuration of the Natanz facility, demonstrating that the attackers possessed extremely detailed information about the facility. Such targeted cyber attacks could become a national-level military weapon and be used in coercion of hostile countries. While U.S. nuclear power plants (NPPs) are well designed and protected and not easily attacked through internet hacking, there have been a few digital-system-related incidents. For example, in 2003, the Slammer worm breached the private network of the Davis Besse plant and disabled a safety monitoring system for almost five hours. The breach did not pose a safety hazard because the plant was offline, but it demonstrates the vulnerability of NPP systems. The worm did not enter the plant systems directly; it began by entering the systems of a Davis Besse contractor, and entered through the T1 line bridging the contractor's computer and Davis Besse's corporate networks. The T1 line was not protected by the plant's firewall. The success of the Stuxnet-Natanz case demonstrates that supply-chain level attack (i.e., physically tampering with digital systems to install undetectable malware for the purpose of bringing harm to a player further down the supply chain network) plays a crucial role in a successful attack. Although attacking an NPP naturally requires more complex strategies and tactics than internet hacking, NPP's have a variety of sophisticated and possibly sensitive systems that may be attacked. Previous studies, for example, Baylon, have identified and confirmed that such attack strategies and tactics can be designed and could produce severe damage to an NPP. Current NPPs were designed according to design basis accidents that predate use of the internet by the general public and did not take into consideration potential cyber attacks that could be carried out by state-sponsored attackers and malicious insiders. To date, no detailed engineering analysis has been performed to realistically examine the consequences these attacks may have on the plants. In this paper, we discuss an approach for assessing the potential consequences of Stuxnet type of attacks. We will examine the engineering criteria used in designing the plants, develop possible attack scenarios in terms of the structures, components, and systems (SSCs) being controlled by digital systems at the NPPs, perform engineering analysis to determine if the attacks would impose conditions that are beyond design bases, and categorize the consequences of the potential accidents. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Learning constitutive relations using symmetric positive definite neural networks

In this work, we present a new neural-network architecture, called the Cholesky-factored symmetric positive definite neural network (SPD-NN), for modeling constitutive relations in computational mechanics. Instead of directly predicting the stress of the material, the SPD-NN trains a neural network to predict the Cholesky factor of the tangent stiffness matrix, based on which the stress is calculated in incremental form. As a result of this special structure, SPD-NN weakly imposes convexity on the strain energy function, satisfies the second order work criterion (Hill's criterion) and time consistency for path-dependent materials, and therefore improves numerical stability, especially when the SPD-NN is used in finite element simulations. Depending on the types of available data, we propose two training methods, namely direct training for strain and stress pairs and indirect training for loads and displacement pairs. We demonstrate the effectiveness of SPD-NN on hyperelastic, elasto-plastic, and multiscale fiber-reinforced plate problems from solid mechanics. The generality and robustness of SPD-NN make it a promising tool for a wide range of constitutive modeling applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

MPC-Based Local Voltage Control Strategy of DGs in Active Distribution Networks

The increasing penetration of distributed generators (DGs) exacerbates the voltage violations in active distribution networks (ADNs). The commonly used centralized voltage control may aggravate the burden of computation and communication. Local voltage control can realize fast response to frequent voltage fluctuations. However, effective local control for DGs still needs to be further investigated. In this paper, a model predictive control (MPC)-based local voltage control strategy of DGs is proposed to cope with voltage violation problems and enhance the adaptation to the DG volatility. DGs are regulated with local Q-V control curves to respond to voltage fluctuations rapidly. Based on the dynamic network partition, a unified control curve for DGs is set in the same area to realize efficient operation. To further improve the performance of voltage control, the parameter tuning of control curves is regularly conducted based on decentralized inter-area coordination with the rolling of the control horizon. Finally, case study is verified on the modified IEEE 33-node distribution system. The proposed local control strategy of DGs can effectively improve the voltage profile and reduce power losses of the whole network without high computation and communication burden.

active distribution network (ADN)↗

Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology

Abstract The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum . The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

59 BASIC BIOLOGICAL SCIENCES↗

Development of high-availability ATCA/PCIe data acquisition instrumentation

Latest Fusion energy experiments envision a quasi-continuous operation regime. In consequence, the largest experimental devices, currently in development, specify high-availability (HA) requirements for the whole plant infrastructure. HA features enable the whole facility to perform seamlessly in the case of failure of any of its components, coping with the increasing duration of plasma discharges (steady-state) and assuring safety of equipment, people, environment and investment. IPFN developed a control and data acquisition system, aiming for fast control of advanced Fusion devices, which is thus required to provide such HA features. The system is based on in-house developed Advanced Telecommunication Computing Architecture (ATCA) instrumentation modules - IO blades and data switch blades, establishing a PCIe network on the ATCA shelf's back-plane. The data switch communicates to an external host computer through a PCIe data network. At the hardware management level, the system architecture takes advantage of ATCA native redundancy and hot swap specifications to implement fail-over substitution of IO or data switch blades. A redundant host scheme is also supported by the ATCA/PCIe platform. At the software level, PCIe provides implementation of hot plug services, which translate the hardware changes to the corresponding software/operating system devices. The paper presents how the ATCA and PCIe based system can be setup to perform with the desired degree of HA, thus being suitable for advanced Fusion control and data acquisition systems. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Metabolic brain networks in aging and preclinical Alzheimer's disease

Metabolic brain networks can provide insight into the network processes underlying progression from healthy aging to Alzheimer's disease. We explore the effect of two Alzheimer's disease risk factors, amyloid-β and ApoE ε4 genotype, on metabolic brain networks in cognitively normal older adults (N = 64, ages 69–89) compared to young adults (N = 17, ages 20–30) and patients with Alzheimer's disease (N = 22, ages 69–89). Subjects underwent MRI and PET imaging of metabolism (FDG) and amyloid-β (PIB). Normal older adults were divided into four subgroups based on amyloid-β and ApoE genotype. Metabolic brain networks were constructed cross-sectionally by computing pairwise correlations of metabolism across subjects within each group for 80 regions of interest. We found widespread elevated metabolic correlations and desegregation of metabolic brain networks in normal aging compared to youth and Alzheimer's disease, suggesting that normal aging leads to widespread loss of independent metabolic function across the brain. Amyloid-β and the combination of ApoE ε4 led to less extensive elevated metabolic correlations compared to other normal older adults, as well as a metabolic brain network more similar to youth and Alzheimer's disease. This could reflect early progression towards Alzheimer's disease in these individuals. Altered metabolic brain networks of older adults and those at the highest risk for progression to Alzheimer's disease open up novel lines of inquiry into the metabolic and network processes that underlie normal aging and Alzheimer's disease.

Neurosciences & Neurology↗

Cross Inference of Throughput Profiles Using Micro Kernel Network Method

Dedicated network connections are being increasingly deployed in cloud, centralized and edge computing and data infrastructures, whose throughput profiles are critical indicators of the underlying data transfer performance. Due to the cost and disruptions to physical infrastructures, network emulators, such as Mininet, are often used to generate measurements needed to estimate throughput profiles, typically expressed as a function of the connection round trip time. The profiles estimated using measurements from such emulated networks are usually inaccurate for high bandwidth and high latency connections, since they do not accurately reflect the critical network transport dynamics mainly due to computing and memory constraints of the host. We present a machine learning (ML) method to estimate the throughput profiles using emulation measurements to closely match the testbed and production network profiles. In particular, we propose a micro Kernel Network (mKN) that provides baseline throughput measurements on the host running Mininet emulations, which are used to learn a regression map that converts them to the corresponding testbed measurement estimates. Once initially learned, this map is applied to measurements from subsequent network emulations on the same host. We present experimental measurements to illustrate this approach, and derive generalization equations for the proposed mKN-ML method. Using a four-site scenario emulation, we show the effectiveness of this method in providing accurate concave throughput profiles from inaccurate convex or non-smooth ones indicated by Mininet emulation.

Rao, Nageswara↗

Software defined network inference with evolutionary optimal observation matrices

A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today's large-scale networks, this is a challenging task due to the scarcity of network measurement resources and the hard constraints that this imposes. Here, this paper proposes a new framework, called SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can lead to the best achievable estimation accuracy using Matrix Completion (MC) techniques. To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function. We evaluate the performance of SNIPER using both synthetic and real network measurement traces from different network topologies and by considering two main applications for per-flow size and delay estimations. Our results show that SNIPER can be applied to a variety of network performance measurements under hard resource constraints. For example, by measuring only 8.8% of all per-flow path delays in Harvard network [1], congested paths can be detected with probability of 0.94. Finally, to demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet.

97 MATHEMATICS AND COMPUTING↗

Learning of networked spreading models from noisy and incomplete data

Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data. Some of the remaining challenges include model selection under the scenarios of unknown network structure, noisy data, missing observations in time, as well as an efficient incorporation of prior information to minimize the number of samples required for an accurate learning. Here, in this work, we introduce a universal learning method based on a scalable dynamic message-passing technique that addresses these challenges often encountered in real data. The algorithm leverages available prior knowledge on the model and on the data, and reconstructs both network structure and parameters of a spreading model. We show that a linear computational complexity of the method with the key model parameters makes the algorithm scalable to large network instances.

97 MATHEMATICS AND COMPUTING↗

Deep nonparametric estimation of operators between infinite dimensional spaces

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

97 MATHEMATICS AND COMPUTING↗