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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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At least 1,297 records · Page 72

Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks

Simulation based scientific applications generate increasingly large amounts of data on high-performance computing (HPC) systems. To allow data to be stored and analyzed efficiently, data compression is often utilized to reduce the volume and velocity of data. However, a question often raised by domain scientists is the level of compression that can be expected so that they can make more informed decisions, balancing between accuracy and performance. In this letter, we propose a deep neural network based approach for estimating the compressibility of scientific data. To train the neural network, we build both general features as well as compressor-specific features so that the characteristics of both data and lossy compressors are captured in training. Our approach is demonstrated to outperform a prior analytical model as well as a sampling based approach in the case of a biased estimation, i.e., for SZ. However, for the unbiased estimation (i.e., ZFP), the sampling based approach yields the best accuracy, despite the high overhead involved in sampling the target dataset.

97 MATHEMATICS AND COMPUTING↗

Direct Prediction of Phonon Density of States With Euclidean Neural Networks

Abstract Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density‐of‐states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high‐quality prediction using a small training set of examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high‐performance thermal storage materials and phonon‐mediated superconductors.

97 MATHEMATICS AND COMPUTING↗

SyReNN: A tool for analyzing deep neural networks

Deep Neural Networks (DNNs) are rapidly gaining popularity in a variety of important domains. Unfortunately, modern DNNs have been shown to be vulnerable to a variety of attacks and buggy behavior. This has motivated recent work in formally analyzing the properties of such DNNs. Furthermore, this paper introduces SyReNN, a tool for understanding and analyzing a DNN by computing its symbolic representation. The key insight is to decompose the DNN into linear functions. Our tool is designed for analyses using low-dimensional subsets of the input space, a unique design point in the space of DNN analysis tools. We describe the tool and the underlying theory, then evaluate its use and performance on three case studies: computing Integrated Gradients, visualizing a DNN’s decision boundaries, and repairing buggy DNNs.

97 MATHEMATICS AND COMPUTING↗

Network Bandwidth Utilization Forecast Model on High Bandwidth Network

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

97 MATHEMATICS AND COMPUTING↗

Advanced Performance Modeling with Combined Passive and Active Monitoring

To improve the efficiency of resource utilization and scheduling of scientific data transfers on high-speed networks, the "Advanced Performance Modeling with combined passive and active monitoring" (APM) project investigates and models a general-purpose, reusable and expandable network performance estimation framework. The predictive estimation model and the framework will be helpful in optimizing the performance and utilization of networks as well as sharing resources with predictable performance for scientific collaborations, especially in data intensive applications. Our prediction model utilizes historical network performance information from various network activity logs as well as live streaming measurements from network peering devices. Historical network performance information is used without putting extra load on the resources by active measurement collection. Performance measurements collected by active probing is used judiciously for improving the accuracy of predictions.

97 MATHEMATICS AND COMPUTING↗

Crowdsourced reliable labeling of safety-rule violations on images of complex construction scenes for advanced vision-based workplace safety

Construction workplace hazard detection requires engineers to analyze scenes manually against many safety rules, which is time-consuming, labor-intensive, and error-prone. Computer vision algorithms are yet to achieve reliable discrimination of anomalous and benign object relations underpinning safety violation detections. Recently developed deep learning-based computer vision algorithms need tens of thousands of images, including labels of the safety rules violated, in order to train deep-learning networks for acquiring spatiotemporal reasoning capacity in complex workplaces. Such training processes need human experts to label images and indicate whether the relationship between the worker, resource, and equipment in the scenes violate spatiotemporal arrangement rules for safe and productive operations. False alarms in those manual labels (labeling no-violation images as having violations) can significantly mislead the machine learning process and result in computer vision models that produce inaccurate hazard detections. Compared with false alarms, another type of mislabels, false negatives (labeling images having violations as “no violations”), seem to have fewer impacts on the reliability of the trained computer vision models.

97 MATHEMATICS AND COMPUTING↗

Visual Analytics of Multivariate Networks With Representation Learning and Composite Variable Construction

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This article presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow with qualitative feedback from experts.

97 MATHEMATICS AND COMPUTING↗

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↗

US LHCNet: Transatlantic Networking for the LHC and the U.S. HEP Community

US LHCNet provides the transatlantic connectivity between the Tier1 computing facilities at the Fermilab and Brookhaven National Labs and the Tier0 and Tier1 facilities at CERN, as well as Tier1s elsewhere in Europe and Asia. Together with ESnet, Internet2, and other R&E Networks participating in the LHCONE initiative, US LHCNet also supports transatlantic connections between the Tier2 centers (where most of the data analysis is taking place) and the Tier1s as needed. Given the key roles of the US and European Tier1 centers as well as Tier2 centers on both continents, the largest data flows are across the Atlantic, where US LHCNet has the major role. US LHCNet manages and operates the transatlantic network infrastructure including four Points of Presence (PoPs) and currently six transatlantic OC-192 (10Gbps) leased links. Operating at the optical layer, the network provides a highly resilient fabric for data movement, with a target service availability level in excess of 99.95%. This level of resilience and seamless operation is achieved through careful design including path diversity on both submarine and terrestrial segments, use of carrier-grade equipment with built-in high-availability and redundancy features, deployment of robust failover mechanisms based on SONET protection schemes, as well as the design of facility-diverse paths between the LHC computing sites. The US LHCNet network provides services at Layer 1(optical), Layer 2 (Ethernet) and Layer 3 (IPv4 and IPv6). The flexible design of the network, including modular equipment, a talented and agile team, and flexible circuit lease management, allows US LHCNet to react quickly to changing requirements form the LHC community. Network capacity is provisioned just-in-time to meet the needs, as demonstrated in the past years during the changing LHC start-up plans.

97 MATHEMATICS AND COMPUTING↗

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↗

Administering truncated receive functions in a parallel messaging interface

Administering truncated receive functions in a parallel messaging interface (`PMI`) of a parallel computer comprising a plurality of compute nodes coupled for data communications through the PMI and through a data communications network, including: sending, through the PMI on a source compute node, a quantity of data from the source compute node to a destination compute node; specifying, by an application on the destination compute node, a portion of the quantity of data to be received by the application on the destination compute node and a portion of the quantity of data to be discarded; receiving, by the PMI on the destination compute node, all of the quantity of data; providing, by the PMI on the destination compute node to the application on the destination compute node, only the portion of the quantity of data to be received by the application; and discarding, by the PMI on the destination compute node, the portion of the quantity of data to be discarded.

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↗