Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “network performance”

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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Prevalence and scalable control of localized networks

The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable, even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers, while incurring a computational cost orders-of-magnitude lower. Here, we validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks, as well as three large empirical networks: synchronization dynamics in the Eastern US power grid, epidemic spreading mediated by the global air-transportation network, and Alzheimer’s disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.

42 ENGINEERING↗
NASA NTRS2024

Launch Complex 39B, SWMU 009, 2023 Performance Monitoring and Air Sparge Expansion Construction Completion Report, Kennedy Space Center, Florida

The 2023 Performance Monitoring and Construction Completion Report (PM-CCR) presents the findings, observations, and results for Air Sparging (AS) operations and expansion activities, as well as sitewide groundwater monitoring for Launch Complex 39B (LC39B), Solid Waste Management Unit (SWMU) 009, at Kennedy Space Center (KSC), Florida. The reporting period for activities covered under this PM-CCR is from January 1, 2023, to December 31, 2023. At LC39B, AS operations began in 2017 in the area west of the launch pad, in the liquid oxygen (LOX) tank area located northwest of the launch pad, and in an area outside of the perimeter fence to protect nearby Outstanding Florida Waters (OFW). The LC39B AS system was installed with 279 AS wells to depths ranging from 23 to 60 feet below land surface (bls), including the sump, correlating to top of screen depths ranging from 20 to 57 feet bls. In December 2022, a total of 22 AS wells were abandoned to support launch pad crane operations, and in November 2023, the system was expanded with five additional AS wells installed to 13 or 17 feet bls near the LOX tank. The remedial objective of the LC39B AS Interim Measure (IM) is to actively decrease concentrations of contaminants of concern (COCs) in groundwater, specifically trichloroethene (TCE), cis-1,2-Dichloroethene (cDCE), and vinyl chloride (VC), to less than their respective Natural Attenuation Default Concentrations (NADCs), so LC39B can transition into a Long-Term Monitoring (LTM) program. This PM-CCR presents the following information for LC39B: • AS system operations and maintenance (O&M) (Year 7 of operation) from January 2023 to December 2023, to include AS trailer relocation in March 2023 and subsequent replacement and re-start in June 2023. • Construction completion details for AS system expansion, which included installation of five new AS wells and one new monitoring well in November 2023. As part of expansion activities, soil samples were also collected for petroleum analysis; no exceedances were identified, and no further investigation for petroleum is warranted. • Performance monitoring results for groundwater sampling events conducted in May/June 2023 (30 wells) and November 2023 (31 wells) in the AS IM area and in the Low Concentration Plume (LCP) areas located north and east of the launch pad for volatile organic compound (VOC) analysis. • Sampling results for one monitoring well, LOX-IW0012S, which is sampled for aluminum on an annual basis (May/June 2023). This well was resampled in November 2023 for both total and dissolved aluminum. Due to a communication error with the laboratory, the May/June 2023 sample was analyzed for total aluminum only. • Groundwater sampling results for per- and polyfluoroalkyl substances (PFAS) collected from seven monitoring wells during the May/June 2023 event to further investigate the Former Sewage Treatment Plant #6 and Percolation Pond area, west of the launch pad. O&M and performance monitoring results show that the AS system at LC39B is operating as designed and meeting performance criteria. Overall runtime was 45 percent (%) during the reporting period (January to December), but the operational runtime was 78% during the timeframe when the system could run (June to December). The most significant downtime contributor was post-launch crane operations following the Artemis launch on November 16, 2022, which lasted until June 2023. During that timeframe, the AS trailer at LC39B was relocated to another KSC remediation site (Wilson Corners) and was subsequently replaced with the AS trailer from the Paint & Oil Locker (POL) remediation site at KSC to resume AS system operations. Performance monitoring results in the AS IM and LCP areas continue to show reduction in COC concentrations over time when compared to baseline levels. In 2023, only one monitoring well (MW0048) detected a COC exceeding its NADC (VC at 740 micrograms per liter [µg/L]), which marks the baseline result for this new well installed during system expansion. Across the rest of the site, VC concentrations have declined or remained stable during the 2023 sampling events. Excluding MW0048, the highest VC result in 2023 was during the May/June sampling event with a concentration of 63 µg/L at MW0032, which is located near MW0048 and the AS expansion area by the LOX tank. TCE was detected in select monitoring wells in the IM area in 2023, but only two locations exceeded the State of Florida Groundwater Cleanup Target Level (GCTL): MW0032 (21 µg/L in May/June 2023 and 9.1 µg/L in November 2023) and MW0036 (5.0 µg/L in November 2023). MW0036 is also located near the LOX tank, on the north side, where the AS system is still operational (Zone Z4). cDCE and trans-1,2-dichloroethene concentrations were less than laboratory method detection limits or their respective GCTLs in all wells sampled in 2023. Near the OFW located northwest of the launch complex, all COC concentrations were less than laboratory method detection limits from monitoring wells (MW0039, MW0040, and LOXTA0002S) sampled in 2023. Aluminum results from LOX-IW0012S, which has been sampled routinely since 2006, detected a total aluminum concentration of 3,900 µg/L during the May/June 2023 sampling event. Results from the November 2023 event detected 5,700 µg/L for total aluminum and 5,500 µg/L for dissolved aluminum. These concentrations slightly decreased from the previous year but remain relatively consistent with historical detections. Aluminum will continue to be sampled on an annual basis at this well as results still exceed the GCTL of 200 µg/L and the Upper Limit of the KSC Background Concentration of 280 µg/L. PFAS results detected nine different PFAS compounds (out of 32 analyzed) from seven wells sampled. Two PFAS compounds, perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), currently have FDEP Provisional GCTLs of 70 nanograms per liter (ng/L). All seven samples collected resulted in concentrations less than the FDEP Provisional GCTLs for both PFAS compounds; no exceedances were observed. PFOS and PFOA also have assigned United States Environmental Protection Agency (USEPA) Maximum Contaminant Levels (MCLs) of 4 nanograms per liter (ng/L). None of the PFOS results exceeded the USEPA MCLs. PFOA was detected in two samples above the USEPA MCL at concentrations of 5.8 ng/L (ECS-IW0009I) and 5.5 ng/L (ECS-IW0009S). Three other PFAS compounds, perfluorohexanesulfonic acid (PFHxS), perfluoro-n-nonanoic acid (PFNA), and hexafluoropropylene oxide dimer acid (GenX), currently have USEPA MCLs of 10 ng/L. PFHxS, PFNA, and GenX were not detected at concentrations greater than their respective USEPA MCLs in any of the seven wells. PFAS compounds without FDEP Provisional GCTLs or USEPA MCLs were screened against USEPA RSLs. No other detections exceeded their respective USEPA RSLs. Additional PFAS sampling will be conducted as part of a future PFAS Site Assessment. Based on O&M activities and performance monitoring, the following is recommended for LC39B: • Continue with Year 8 AS system operation within Zone Z4, which includes the AS expansion area. Zone Z3, which has been off since 2018, should remain off as no rebound has been observed. Zones Z1 and Z2, which were turned off at the end of 2022, will remain shut down as monitoring well results have consistently been below GCTLs or have low-level detections with stable or decreasing trends (Meeting Minute 2402-M10, Decision 2402-D30). • Continue with performance monitoring in 2024 with the same monitoring well network as 2023, except with the addition of MW0048 in both semi-annual events. Baseline concentrations for this well were collected during the November 2023 performance monitoring event. Semi-annual sampling should be planned for the May 2024 and November 2024 timeframes (Meeting Minute 2402-M10, Decision 2402-D31). • Continue sampling monitoring well, LOX-IW0012S, for aluminum (total and dissolved) on an annual basis in May 2024. It is also recommended to re-develop this well prior to the next sampling event (Meeting Minute 2402-M10, Decision 2402-D32). The above recommendations for LC39B were presented at the February 2024 KSCRT Meeting, with Team consensus reached on the path forward. The contents of this PM-CCR were also presented at this meeting.

Deborah M Wilson↗
DOE OSTI2020

Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design

In recent years, the CNNs have achieved great successes in the image processing tasks, e.g., image recognition and object detection. Unfortunately, traditional CNN's classication is found to be easily misled by increasingly complex image features due to the usage of pooling operations, hence unable to preserve accurate position and pose information of the objects. To address this challenge, a novel neural network structure called Capsule Network has been proposed, which introduces equivariance through capsules to signicantly enhance the learning ability for image segmentation and object detection. Due to its requirement of performing a high volume of matrix operations, CapsNets have been generally accelerated on modern GPU platforms that provide highly optimized software library for common deep learning tasks. However, based on our performance characterization on modern GPUs, CapsNets exhibit low effciency due to the special program and execution features of their routing procedure, including massive unshareable intermediate variables and intensive syn- chronizations, which are very dicult to optimize at software level. To address these challenges, we propose a hybrid computing architecture design named PIM-CapsNet. It preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's ineffciency by leveraging the processing-in-memory capability of today's 3D stacked memory. Using routing procedure's inherent parallellization feature, our design enables hierarchical improvements on CapsNet inference effciency through minimizing data movement and maximizing parallel processing in memory. Evaluation results demonstrate that our proposed design can achieve substantial improvement on both performance and energy savings for CapsNet inference, with almost zero accuracy loss. The results also suggest good performance scalability in optimizing the routing procedure with increasing network size.

Zhang, Xingyao↗
NASA NTRS1994

Analysis and Performance of a 12-Pulse High Power Regulator

Under work being performed to upgrade the 20 Kilowatt CW uplink transmitters of the NASA Deep Space Network (DSN), the high voltage regulator has been revisited in order to optimize its performance (long-term stability and regulation), and enhance field reliability.

power regulation high voltage power supply Klystro↗
DOE OSTI2022

Machine Learning-based False Data Injection Attack Detection and Localization in Power Grids

Cyberattacks on critical infrastructures can be catastrophic and bring nations to their knees. Therefore, detecting these attacks is crucial and challenging. This paper presents a novel approach for detecting and locating cyberattacks affecting an electrical power system. The adversary employs a man-in-the-middle technique to inject false data into the communication between distributed energy resources (DER) and Microgrid Controller (MGC) with the goal of disrupting power delivery. The approach for detection and localization is based on integrating multiple machine learning-based anomaly detection models that combine network traffic data and grid measurements. Experiments are performed to assess the method's performance using a hardware-in-the-loop real-time simulation testbed which includes Modbus TCP/IP communication. Power system topology and operating conditions are based on actual topology and real-world data provided by the Holy Cross Energy utility network. Results confirm that the method can be successfully employed for detecting and localizing cyberattacks.

Leao, Bruno P.↗
DOE OSTI2022

Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

Solar energetic particles (SEPs) are an essential source of space radiation, and are hazardous for humans in space, spacecraft, and technology in general. In this paper, we propose a deep-learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs, and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine-learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data

Growing vaccine hesitancy is contributing to the decline in immunization rates for highly contagious, vaccine-preventable childhood diseases. Therefore, there has been a significant interest in understanding how hesitancy is spreading at higher spatio-temporal resolutions, enabling more targeted interventions. Motivated by this, we study the problem of prediction of vaccine hesitancy at the ZIP Code level, referred to as the VaxHesitancy problem. A significant challenge for this problem is the lack of high-resolution data that indicates hesitancy. Here, we develop a hybrid VaxHesSTL framework that combines a Graph Neural Network (GNN) and a Recurrent Neural Network (RNN) to address the VaxHesitancy problem. The GNN uses a ZIP Code-level network to capture spatial signals from neighboring areas, while the RNN models the temporal dynamics present in the data. We train and evaluate VaxHesSTL using a large dataset, namely the All-Payer Claims Databases (APCD), for Virginia, consisting of insurance claims from over five million individuals for six years. We find that an aggregated contact network or graph, developed from a detailed activity-based population network, plays an important role in the performance of VaxHesSTL, compared to graph models based solely on spatial proximity. Experiments demonstrate that VaxHesSTL outperforms a range of state-of-the-art baselines, which rely solely on historical time series data without accounting for spatial relationships. Since hesitancy data at higher spatial resolution is often unavailable or hard to get, we incorporate an active learning approach with our VaxHesSTL framework to optimize the training set without compromising the prediction performance. We find that hesitancy data for only 18% of ZIP Codes selected by active learning allows us to forecast hesitancy for all the ZIP Codes in the Virginia.

60 APPLIED LIFE SCIENCES↗
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 enables 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.

hybrid simulations↗
DOE OSTI2021

Physics-Based Optical Neuromorphic Classification

Typical approaches to classify scenes from light convert the light field to electrons to perform the computation in the digital electronic domain. This conversion and downstream computational analysis require significant power and time. Diffractive neural networks have recently emerged as unique systems to classify optical fields at lower energy and high speeds. Previous work has shown that a single layer of diffractive metamaterial can achieve high performance on classification tasks. In analogy with electronic neural networks, it is anticipated that multilayer diffractive systems would provide better performance, but the fundamental reasons for the potential improvement have not been established. In this work, we present extensive computational simulations of two - layer diffractive neural networks and show that they can achieve high performance with fewer diffractive features than single layer systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

The application of integrated knowledge-based systems for the Biomedical Risk Assessment Intelligent Network (BRAIN)

One of NASA's goals for long duration space flight is to maintain acceptable levels of crew health, safety, and performance. One way of meeting this goal is through BRAIN, an integrated network of both human and computer elements. BRAIN will function as an advisor to mission managers by assessing the risk of inflight biomedical problems and recommending appropriate countermeasures. Described here is a joint effort among various NASA elements to develop BRAIN and the Infectious Disease Risk Assessment (IDRA) prototype. The implementation of this effort addresses the technological aspects of knowledge acquisition, integration of IDRA components, the use of expert systems to automate the biomedical prediction process, development of a user friendly interface, and integration of IDRA and ExerCISys systems. Because C language, CLIPS and the X-Window System are portable and easily integrated, they were chosen ss the tools for the initial IDRA prototype.

Loftin, Karin C.↗
DOE OSTI2025

Role of Intersections in Fracture Connectivity

Networks of intersecting fractures often provide the flow paths through subsurface reservoirs. Assessing network connectivity is challenging because fracture intersections compose a vanishingly small fraction of the network void volume. In this paper, motivated by 3D X-ray imaging of the simplest element of fracture network, that is, two orthogonal fractures, we perform a percolation and finite-size scaling analysis to study the connectivity provided by fracture intersections. The conditions when an intersection enhances connectivity across a sample depend on spatial correlations in the fracture aperture distributions, on the stress state, and on the direction of flow. Here we consider three flow directions: (a) across intersections, (b) parallel to intersections and (c) around corners. For (a), intersections provide minimal enhancement of connectivity because they contribute little additional void area. For (b), intersections increase the probability of a connected path near threshold by enabling 3D connected pathways that are not possible in parallel fractures. Flow around corners, (c), is fundamentally the result of the intersection connecting two fractures in series and spatial correlations are broken around corners, suppressing the connectivity relative to (a). When the connected fractures are stressed equally, a joint percolation threshold emerges that continues to have scale invariance. However, when the fractures are stressed unequally, the system has mixed percolation without clearly defined percolation thresholds. In all cases, percolation probabilities are found to be scale dependent which has important consequences for the connectivity of larger fracture networks composed of the fundamental element studied here.

02 PETROLEUM↗
NASA NTRS2005

Biology-Inspired Distributed Consensus in Massively-Deployed Sensor Networks

Promises of ubiquitous control of the physical environment by large-scale wireless sensor networks open avenues for new applications that are expected to redefine the way we live and work. Most of recent research has concentrated on developing techniques for performing relatively simple tasks in small-scale sensor networks assuming some form of centralized control. The main contribution of this work is to propose a new way of looking at large-scale sensor networks, motivated by lessons learned from the way biological ecosystems are organized. Indeed, we believe that techniques used in small-scale sensor networks are not likely to scale to large networks; that such large-scale networks must be viewed as an ecosystem in which the sensors/effectors are organisms whose autonomous actions, based on local information, combine in a communal way to produce global results. As an example of a useful function, we demonstrate that fully distributed consensus can be attained in a scalable fashion in massively deployed sensor networks where individual motes operate based on local information, making local decisions that are aggregated across the network to achieve globally-meaningful effects.

Jones, Kennie H.↗
NASA NTRSDate not supplied

Neo – Mars Adaptive Training Integrative Knowledge System (MATRIKS) to Improve Operational Performance and its Neural Basis for Spaceflight

With prolonged mission durations, spaceflight crews will become increasingly dependent on onboard technologies for knowledge acquisition and maintenance. It is expected that not all skills and knowledge required for these missions can be retained and retrieved based on pre-mission training alone. Limited and delayed communication will significantly constrain support from Mission Control and crews will increasingly rely on autonomous onboard technologies to successfully perform post-landing operations. With the present project we will target NASA’s particular interest in developing and assessing an adaptive, just-in-time countermeasure that will consolidate and improve skills that are most relevant to space flight operations. To achieve this aim, NASA established a Virtual NASA Specialized Center of Research (VNSCOR)referred to as “Mars Adaptive Training Integrative Knowledge System (MATRIKS)”, comprising the following three projects: (1) “Trinity–Multi-Environment Virtual Training for Long Duration Exploration Missions”, PI: A. Anderson (UC Boulder); (2) “Morpheus–A Haptic Sensory Supplement to Optimize In-Flight Adaptive Training for Human Control of Spacecraft Robotic Arms”, PI: S. Robinson, UC Davis); and the present project “Neo–Adaptive Training integrative knowledge System to Improve Operational Performance and its Neural Basis for Spaceflight” (UPenn, PI: A.C. Stahn). Neo leverages a validated workstation called 6DF that simulates a rendezvous and docking maneuver using real spacecraft flight dynamics. It is designed to (1) train and improve sensorimotor skills relevant for inflight and post-landing operational tasks; (2) feature an autonomous and adaptive training approach that does not rely on feedback from flight operations on the ground; (3) maximize the transfer of mission-relevant motor skills; (4) allow the assessment of the neural circuitry underlying the task; and (5) deliver the training in a motivating and meaningful way to astronauts. Neocomprises two overarching aims: First, we will identify the neural circuitry underlying spaceflight relevant tasks by performing a subset of the 6DFtaskduring functional magnetic resonance imaging (MRI)in a total of up to N=30 subjects with varying levels of 6DF training experience. Second, as part of the above-mentioned VNSCOR MATRIKS the proposed 6DF autonomous intelligent tutor system will be integrated in an additive manner with a haptic feedback intervention (Morpheus), and a multi-environment virtual trainer(Trinity).It is expected that Neo, Morpheus and Trinity mutually complement each other to facilitate an effective countermeasure tool to acquire and retain operational skills that are critical for exploration class missions. To assess the efficacy of this combined effort, the VNSCOR MATRIKS will collect data inN=16 crew members in one HERA campaign of 45 days duration with N=16 crew members(four missions with N=4 crew member seach).The primary goal is to identify changes in operational performance as assessed by NASA’s simulator of Canadarm2 operations, i.e., Robotic On-board Trainer (ROBoT-r) in response to MATRIKS. As part of Neo we will also identify if, and to what extent MATRIKS will promote transfer to general cognitive performance (Cognition battery), distinctive visuo-spatial tasks critical for telerobotic tasks (Spatial Cognition battery), and affect brain structural changes and the neural circuitry of key brain networks expected to be relevant for spaceflight-related performance. At the conclusion of the research, we will have defined and demonstrated the use of a neuroscience-based, adaptive training integrative knowledge system to potentially mitigate visuo-spatial and sensorimotor brain changes associated with prolonged isolation and confinement to reduce the likelihood or impact of potential decrements in human performance capabilities during long-duration space missions. The expected significance of this 4-year project relates to its relevance for facilitating effective countermeasure tools to acquire and retain operational skills that are critical for exploration class missions. This will support the development of necessary countermeasures and technologies in support of human space exploration, focusing on mitigating operational performance risks.

A C Stahn↗
DOE OSTI2023

Data for "Enhancing 2-Pyrone Synthase Efficiency by High-Throughput Mass-Spectrometric Quantification and In Vitro/In Vivo Catalytic Performance Correlation"

Engineering efficient biocatalysts is essential for metabolic engineering to produce valuable bioproducts from renewable resources. However, due to the complexity of cellular metabolic networks, it is challenging to translate success in vitro into high performance in cells. To meet such a challenge, an accurate and efficient quantification method is necessary to screen a large set of mutants from complex cell culture and a careful correlation between the catalysis parameters in vitro and performance in cells is required. In this study, we employed a mass-spectrometry based high-throughput quantitative method to screen new mutants of 2-pyrone synthase (2PS) for triacetic acid lactone (TAL) biosynthesis through directed evolution in E. coli. From the process, we discovered two mutants with the highest improvement (46 fold) in titer and the fastest kcat (44 fold) over the wild type 2PS, respectively, among those reported in the literature. A careful examination of the correlation between intracellular substrate concentration, Michaelis-Menten parameters and TAL titer for these two mutants reveals that a fast reaction rate under limiting intracellular substrate concentrations is important for in-cell biocatalysis. Such properties can be tuned by protein engineering and synthetic biology to adopt these engineered proteins for the maximum activities in different intracellular environments.

catalysis↗
DOE OSTI2023

Enhancing Qubit Readout with Autoencoders

In addition to the need for stable and precisely controllable qubits, quantum computers take advantage of good readout schemes. Superconducting qubit states can be inferred from the readout signal transmitted through a dispersively coupled resonator. Here, this work proposes a readout classification method for superconducting qubits based on a neural network pretrained with an autoencoder approach. A neural network is pretrained with qubit readout signals as autoencoders in order to extract relevant features from the data set. Afterward, the pretrained-network inner-layer values are used to perform a classification of the inputs in a supervised manner. We demonstrate that this method can enhance classification performance, particularly for short- and long-time measurements where more traditional methods present inferior performance.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Spike-and-Slab Shrinkage Priors for Structurally Sparse Bayesian Neural Networks

Network complexity and computational efficiency have become increasingly significant aspects of deep learning. Sparse deep learning addresses these challenges by recovering a sparse representation of the underlying target function by reducing heavily overparameterized deep neural networks. Specifically, deep neural architectures compressed via structured sparsity (e.g., node sparsity) provide low-latency inference, higher data throughput, and reduced energy consumption. In this article, we explore two well-established shrinkage techniques, Lasso and Horseshoe, for model compression in Bayesian neural networks (BNNs). To this end, we propose structurally sparse BNNs, which systematically prune excessive nodes with the following: 1) spike-and-slab group Lasso (SS-GL) and 2) SS group Horseshoe (SS-GHS) priors, and develop computationally tractable variational inference, including continuous relaxation of Bernoulli variables. We establish the contraction rates of the variational posterior of our proposed models as a function of the network topology, layerwise node cardinalities, and bounds on the network weights. Furthermore, we empirically demonstrate the competitive performance of our models compared with the baseline models in prediction accuracy, model compression, and inference latency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Comparative Study of Physics‐Informed and Data‐Driven Neural Networks for Compound Flood Simulation at River‐Ocean Interfaces: A Case Study of Hurricane Irene

Simulating compound flooding (CF) at the river-ocean interface within large-scale Earth System Models (ESMs) presents significant challenges due to complex interactions between river discharge, storm surge, and tides. This study assesses the comparative advantages of physics-informed and data-driven machine learning (ML) approaches for enhancing local ESM performance. We systematically compare data-driven neural network models (i.e., CNNs, U-Net, Long Short-Term Memory (LSTM), Gated Recurrent Unit), and physics-informed neural network (PINN) models, including vanilla PINN and a finite-difference-based PINN (FD-PINN). Specifically, FD-PINN is introduced to enhance computational efficiency, accelerating vanilla PINNs by ∼6.5 times while improving accuracy. To enhance data-driven model training, a new data-generation approach is developed to sample historical fluvial and coastal flood events, which ensures a robust data set for extreme event prediction. The models are evaluated using a realistic one-dimensional river domain extracted from an ESM's river mesh and the Hurricane Irene event as an independent test case. Results show that FD-PINN achieves accurate predictions with significantly reduced computational costs relative to vanilla PINNs. Among data-driven models, the best overall performance is achieved by a CNN-LSTM hybrid, which balances accuracy and efficiency. While a fully connected CNN (CNN-FC) provides the best accuracy, it incurs high computational cost. Architectures lacking strong temporal modeling tend to underperform on unseen events. These findings highlight the importance of sequence-aware designs for robust generalization. This study reveals the trade-offs between physics-informed and data-driven models and proposes an adaptive hybrid framework for integrating ML into ESMs to enhance local flood simulations.

Earth Systems Modeling↗