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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Autonomous, Context-Sensitive, Task Management Systems and Decision Support Tools I: Human-Autonomy Teaming Fundamentals and State of the Art

Recent advances in artificial intelligence, machine learning, data mining and extraction, and especially in sensor technology have resulted in the availability of a vast amount of digital data and information and the development of advanced automated reasoners. This creates the opportunity for the development of a robust dynamic task manager and decision support tool that is context sensitive and integrates information from a wide array of on-board and off aircraft sourcesa tool that monitors systems and the overall flight situation, anticipates information needs, prioritizes tasks appropriately, keeps pilots well informed, and is nimble and able to adapt to changing circumstances. This is the first of two companion reports exploring issues associated with autonomous, context-sensitive, task management and decision support tools. In the first report, we explore fundamental issues associated with the development of an integrated, dynamic, flight information and automation management system. We discuss human factors issues pertaining to information automation and review the current state of the art of pilot information management and decision support tools. We also explore how effective human-human team behavior and expectations could be extended to teams involving humans and automation or autonomous systems.

context-sensitive↗
NASA NTRSDate not supplied

Market Survey 2020: Commercial Clinical Decision Support Systems and Wellness Tools

For long-duration, deep space exploration missions, current methods for managing and supporting crew health and medical conditions will be unsuitable. Communication and data transmission lags will necessitate the use of a sophisticated clinical decision support system (CDSS) that will tailor diagnosis and treatment guidance that is context-sensitive for anticipated astronaut health, wellness, and medical conditions. A variety of clinical decision support (CDS) and wellness tools (WT) are currently available in the commercial market and a broad-brush survey of this market can provide an initial impression of the current state of the art which, in turn, can inform the roadmap of NASA deep space CDSS development and associated requirements. Such a survey was undertaken during the first six months of 2020 using directed convenience sampling to obtain information provided by vendors on their websites; both commercially available CDS and WT (such as those used to track and monitor nutrition, exercise, and sleep) were included. Areas assessed were item type (e.g., software/application, device); primary purpose of the item (e.g., diagnostic support, nutrition tracking); additional purposes (if any); reported features, capabilities, and functionality; setting of use (e.g., inpatient, outpatient); intended user (e.g., clinician, patient); location and sources of data/information used or produced by the item; integration with patient electronic health record (EHR); compliance with interoperability ontologies and standards (e.g., Health Level 7 [HL7], Systematized Nomenclature of Medicine – Clinical Terminology [SNOMED-CT]); and whether the item is knowledge-based (derived from research findings) or non-knowledge-based (derived through artificial intelligence, machine learning, advanced probability and statistics), among others. Ninety-seven (97) vendor websites describing 196 CDS and 73 WT (269 total) were reviewed and coded. The primary purpose of the majority of CDS reviewed is diagnosis or diagnosis/treatment/drug decision support—targeted for clinician use— and the primary purpose of the majority of WT reviewed is the monitoring of different health metrics, most often through the use of a biosensor device (e.g., blood pressure)—targeted for patient use. Very few CDS or WT appear to comply with major international interoperability standards or can be integrated with a patient’s EHR data. None consider contextual factors, such as conditions of the physical environment (e.g., CO2 levels). The majority of CDS and WT reviewed are non-knowledge, cloud- or web-based applications or software. Forty-three (43) major findings were identified and the implications those findings have for NASA will be discussed. Example major findings include: CDS-WT capabilities range from diagnosis to treatment applications, CDS-WT may be wearable or non-wearable and are technologically advanced and only a few CDS-WT tools referenced compliance to ensure interoperability, among other findings. Recommendations will also be offered that will help to address ExMC Gap, Medical-701: Enhance medical capabilities within an exploration medical system.

market survey↗
NASA NTRSDate not supplied

Decision Support Tool for Risk Assessment & Maneuver Planning in Collision Avoidance

As the quantity of orbital debris continues to grow, so too does the rate of conjunction messages that suggest possible collisions between high value payloads and debris. The abundance of these conjunction messages, and eventual misses, has led to a culture of ignored alerts, and an increase in satellite operation costs as a result of the frequent need to plan resources for maneuver planning and execution. The loss of “trust” in conjunction alerts is due to the poorly characterized evolution in probability of collision (Pc) as time approaches the time of closest approach (TCA) between two objects, as well as the interpretation of Pc in the context of maneuver planning. To address these problems, and in collaboration with the NASA Conjunction Assessment Risk Analysis (CARA) program, the Industrial Sciences Group has developed a novel Maneuver Decision Support System (MDSS) to assist satellite operations in conjunction assessment and Maneuver planning. It provides a meaningful and intuitive Urgency metric for actionable maneuver decisions, based on the physical dynamics of conjunctions. It is based on a forecast of the evolution of Pc over time and represents an advance over current methods that are in use for satellite conjunction monitoring and planning. The result is to give satellite operators a validated decision support systems to plan for maneuver execution or mitigation or monitoring up to 3 days before TCA.

Decision Support Tool↗
NASA NTRSDate not supplied

Clinical Decision Support Project

As NASA plans for exploration missions into deep space, significant challenges are realized due to the distance from Earth. Beside the effects of microgravity and radiation exposure, the astronauts face the additional constraints of isolation, lack of resupply, increasingly difficult evacuation, and delayed and disrupted communication with ground-based medical care providers. These constraints require a paradigm shift from current medical care where crews rely on the real-time communications with ground-based medical care providers toward Earth-independent medical operations for astronaut medical care. Medical expertise and decision-making are ground-based for current International Space Station and planned Lunar missions. However, a deep space exploration crew will need to autonomously perform the detection, diagnosis, treatment, and prevention of medical conditions. One approach to provide Earth-independent medical operations is to augment the requisite knowledge, skills, and abilities (KSAs) of a time-constrained crew—operating under stressful conditions, combatting fatigue, and facing a potential medical crisis—with a robust clinical decision support system (CDSS). The CDSS is envisioned as an integrated, software-based tool deployed on a laptop computer or handheld device. The CDSS will assist the crew and ground support when interacting with knowledge/data bases (e.g. records, pharmacy, schedule), instrumentation (e.g. imaging, physiological monitoring devices), and habitat (e.g. wellness system, task performance system) and vehicle systems (e.g. environmental system, communication system). In addition, the human interface will employ a context-based approach that accounts for the crew’s situation. Thus, extraneous and clinically/operationally non-relevant information are reduced to avoid an increase in cognitive load. The framework of an ideal spaceflight CDSS is to include core and advanced analytical features that maintain a flexible platform for integrating new technology in the future. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) is expanding the boundaries of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by actively identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addressed ap Medical-701 within the Inflight Medical Conditions risk: “We need to increase inflight medical capabilities and identify new capabilities that (a) maximize benefit and/or (b) reduce “costs” on human system/mission/vehicle resources.” Though mass, volume, and power will face increasing constraints, the projected computational capabilities of spacecraft systems will increase exponentially as information technology advances in this decade and beyond. Hence, data, software, and computational resources will play an essential and synergistic role in maintaining crew health, wellness, and performance in deep space missions. The focus of the CDS project was to develop recommended requirements for an in-vehicle CDSS that acts as a ‘virtual assistant’ for delivering optimal health, performance, and medical care during exploration missions. In fiscal year 2022 (FY22), the CDS project was chartered to baseline and/or revise all CDS project related documentation and update the CDS project model to include the revised CDSS Concept of Operations, revised systems-based modeling language (SysML) activity diagrams, and baseline requirements. The focus of this presentation will be an overview of the CDS products and CDS model content.

Decision Support↗
NASA NTRSDate not supplied

Lower Mekong Hydrological Decision Support system

The Lower Mekong Hydrological Decision Support system (LMHDSs) is a environmental data analysis tool developed at the NASA Goddard Space Flight Center with funding from the SERVIR Applied Sciences Team and technical support from SERVIR Science Coordination Office (SCO). The web application allows stakeholders and decision-makers to view and download the inputs and outputs to the Soil and Water Assessment Tool(SWAT) model temporally and spatially. The front end is developed using JavaScript libraries like OpenLayers and Stock charts and the backend uses Django, a Python-based web framework. The web app provides several features, including visualizing map products, time-series plots, land-use/land-cover and associated soil information, and a data cart for downloading data. In addition, LMHDSs incorporates the NASAaccess software package, which provides seamless access to various climate and weather data products from NASA’s Earth observations portfolio. The application is region agnostic (any valid SWAT model can be used), modular (different components of the applications can be customized), and open (anyone can download and run it on their end). The web app is currently in use by the Mekong River Commission (MRC), a treaty-based regional intergovernmental organization that is made up of Mekong countries, as part of its hydrological decision support.

Hydrology↗
NASA NTRSDate not supplied

A Decision Support System for Extravehicular Operations Under Significant Communication Latency

Within the next few decades, humanity hopes to perform extravehicular activities (EVAs) on the surface of Mars; however, several technical and operational challenges must first be overcome. Foremost among these challenges is managing a significant two-way communication latency between Earth and Mars. Current and historical paradigms of EVA operations have required near-real-time communication between the crewmember(s) performing an EVA and an Earth-based mission control. Next-generation operational paradigms for supporting deep space exploration will necessitate a distributed decision authority system, including delayed Earth-based mission control, the on-planet extravehicular crewmember(s), and intermediate mission support from intravehicular crewmember(s) within real-time communication range. This latter group is of particular interest: they must provide operations support without the plentiful resources available to mission control on Earth. For this purpose, NASA is developing the Personalized EVA Informatics and Decision Support (PersEIDS) software platform. PersEIDS is designed to bolster operator situational awareness and offload operator workload by automating the tracking and projection of consumables usage over an EVA timeline, providing real-time probabilistic safety assessments of an EVA timeline given consumables constraints, and recommending alternative EVA timeline(s) when the active timeline is not expected to be completed under consumables limits. The PersEIDS concept of operations, use cases, and models will be presented. A limited version of PersEIDS was demonstrated during a three-day-long study where each day a roughly four-hour-long simulated Martian EVA was performed in virtual reality at the NASA Johnson Space Center. The first day was a control trial without PersEIDS support; the second and third days represented different levels of decision support provided by PersEIDS to the intravehicular crewmember acting as mission control. With PersEIDS support, the IV crewmember was able to manage the mission to completion faster and with more remaining consumables; however, additional testing is required to understand confounding factors, e.g. training bias.

Mars↗
NASA NTRS2022

NASA’s Global Precipitation Measurement Mission: Leveraging Stakeholder Engagement & Applications Activities to Inform Decision-making

The application of satellite precipitation estimates from NASA’s Global Precipitation Measurement (GPM) Mission for decision-making has been a focus for the mission since launch. As a result, GPM data have enabled a range of applications that address societal needs, including water resource management, crop forecasting, ecological monitoring, disaster response, public health, aviation, weather forecasting, and climate modeling, among others. GPM applications activities have continued to focus on user engagement through in person trainings and interviews, workshops, webinars, and educational outreach activities. The goals of these efforts are to synthesize community data needs in order to effectively support and enable decision-making across agencies, academia and the global community. While these efforts have helped the GPM mission establish a large stakeholder community that encompasses federal and state partners, academic institutions, nd private and nonprofit companies, there remains difficulties associated with accessing, processing, and applying the data to support or enable applications. In this article, we present GPM applications strategies and approaches used to enhance the applications value of GPM data, and most importantly, demonstrate how these efforts have and can inform different decision-making contexts. This work also provides a discussion on key lessons learned from the user community and how this information can be utilized to help better support and shape applications approaches for future NASA Earth Science missions.

Satellite precipitation↗
NASA NTRSDate not supplied

A Decision Support System for Extravehicular Operations Under Significant Communication Latency

Humanity hopes to perform extravehicular activities (EVAs) on the surface of Mars; however, several technical and operational challenges must first be overcome. Foremost among these challenges is managing a significant communication latency between Earth and Mars. Current and historical paradigms of EVA operations have required near-real-time communication between the crewmember(s) and Earth-based mission control. Nextgeneration operational paradigms for supporting deep space exploration will necessitate a distributed decision authority system, including delayed Earth-based mission control, the onplanet extravehicular crewmember(s), and intermediate mission support from intravehicular (IV) crewmember(s) within real-time communication range. This latter group is of particular interest: they must provide operations support without the plentiful resources available to mission control on Earth. Thus, NASA is developing the Personalized EVA Informatics and Decision Support (PersEIDS) software platform. PersEIDS is designed to bolster operator situational awareness and offload operator workload by automatically tracking and projecting consumables usage over an EVA timeline, providing real-time probabilistic safety assessments and recommending alternative EVA timeline(s) when the active timeline is not expected to be completed under consumables limits. The PersEIDS concept of operations, use cases, and models will be presented. A limited version of PersEIDS was demonstrated during a three-day-long study where each day a roughly four-hour-long simulated Martian EVA was performed in virtual reality at the NASA Johnson Space Center. The first day was a control trial without PersEIDS support; the second and third days represented different levels of decision support provided by PersEIDS to the IV crewmember acting as mission control. With PersEIDS support, the IV crewmember was able to manage the mission to completion faster and with more remaining consumables; however, additional testing is required to understand confounding factors, e.g., training bias.

Mars↗
NASA NTRSDate not supplied

PersEIDS: A Biomedical Decision Support System for Extravehicular Operations Under Significant Communication Latency

Humanity hopes to perform extravehicular activities (EVAs) on the surface of Mars; however, several technical and operational challenges must first be overcome. Foremost among these challenges is managing a significant communication latency between Earth and Mars. Current and historical paradigms of EVA operations have required near-real-time communication between the crewmember(s) and Earth-based mission control. Nextgeneration operational paradigms for supporting deep space exploration will necessitate a distributed decision authority system, including delayed Earth-based mission control, the onplanet extravehicular crewmember(s), and intermediate mission support from intravehicular (IV) crewmember(s) within real-time communication range. This latter group is of particular interest: they must provide operations support without the plentiful resources available to mission control on Earth. Thus, NASA is developing the Personalized EVA Informatics and Decision Support (PersEIDS) software platform. PersEIDS is designed to bolster operator situational awareness and offload operator workload by automatically tracking and projecting consumables usage over an EVA timeline, providing real-time probabilistic safety assessments and recommending alternative EVA timeline(s) when the active timeline is not expected to be completed under consumables limits. The PersEIDS concept of operations, use cases, and models will be presented. A limited version of PersEIDS was demonstrated during a three-day-long study where each day a roughly four-hour-long simulated Martian EVA was performed in virtual reality at the NASA Johnson Space Center. The first day was a control trial without PersEIDS support; the second and third days represented different levels of decision support provided by PersEIDS to the IV crewmember acting as mission control. With PersEIDS support, the IV crewmember was able to manage the mission to completion faster and with more remaining consumables; however, additional testing is required to understand confounding factors, e.g., training bias.

Mars↗
DOE OSTI2022

Effects of random forest modeling decisions on biogeochemical time series predictions

Abstract Random forests (RF) are an increasingly popular machine learning approach used to model biogeochemical processes in the Earth system. While RF models are robust to many assumptions that complicate deterministic models, there are several important parameterization decisions for appropriate use and optimal model fit. We explored the role that parameter decisions, including training/testing data splitting strategies, variable selection, and hyperparameters play on RF goodness‐of‐fit by constructing models using 1296 unique parameter combinations to predict concentrations of nitrate, a key nutrient for biogeochemical cycling in aquatic ecosystems. Models were built on long‐term, publicly available water quality and meteorology time series collected by the National Estuarine Research Reserve monitoring network for two contrasting ecosystems representing freshwater and brackish estuaries. We found that accounting for temporal dependence when splitting data into training and testing subsets was key for avoiding over‐estimation of model predictive power. In addition, variable selection, the ratio of training to testing data, and to a lesser degree, variables per split and number of trees, were significant parameters for optimizing RF goodness‐of‐fit. We also explored how model parameter decisions influenced interpretation of the relative importance of predictors to the model, and model predictor‐dependent variable relationships, with results suggesting that both data structure and model parameterization influence these factors. Because much of the current RF literature is written for the computational and statistical science communities, the primary goal of this study is to provide guidelines for aquatic scientists new to machine learning to apply RF techniques appropriately to aquatic biogeochemical datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Communities in energy transition: exploring best practices and decision support tools to provide equitable outcomes

Abstract The U.S. coal industry has been in a state of decline for the past decade, a trend ushered by flat electricity demand, increased regulatory pressure, and market competition from cost-competitive clean energy sources. The receding economic viability of the coal industry has been acutely felt by the communities with immediate economic ties to coal-fired generation. With the energy transition underway, the question of how to engage communities as stakeholders in the decision-making process and address their needs through an equitable and just transition remains unresolved. To that end, this paper explores the economic, environmental, and social challenges presented by the energy transition at the community level, highlighting four case studies from transitioning coal-dependent communities across the United States to ultimately identify best practices in coal plant decommissioning processes. This paper weaves these community-identified best practices into two support tools—a decommissioning checklist and a redevelopment decision-making framework—that can be used to engage communities in the power plant retirement decision, the site reclamation phase, and eventual redevelopment of the site and revitalization of the surrounding community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Farmer characteristics and decision-making: A model for bioenergy crop adoption

We report the commercial development of biofuels and bioproducts depends on whether renewable biomass feedstock is available while not directly competing with the production of food. Farmers are one of the most important stakeholders in the biofuel supply chain and confront a range of uncertainties while entering the bioenergy market. Their decision-making process is extremely complex and rarely purely rational. Modeling farmer behavior requires considering a wide range of individual-level factors, socio-temporal dynamics, institutional settings, and their interactions. These characteristics make agent-based modeling a suitable framework for evaluating such systems. We developed a model to simulate farmer bioenergy crop adoption behavior across a 50-county study region in Nebraska, Kansas, and Colorado. The analysis considers adoption decisions for two bioenergy feedstocks, crop residues and energy crops. We examine the influence of individual and farm characteristics, market structure, social networks, and media influence on farmer adoption decisions. Our results indicate that different factors can have varied impacts on the speed of adoption for the crop residues and energy crops. Identifying levers that have the most impact on grower adoption can inform the design of interventions both from policy and private sector standpoints with important implications for the future the bioenergy industry.

09 BIOMASS FUELS↗
DOE OSTI2022

Economic dispatch for electricity merchant with energy storage and wind plant: State of charge based decision making considering market impact and uncertainties

Here this paper investigates how the market impact of electricity merchants and uncertainty of wind generation affect their co-optimized scheduling policy, specifically for merchants who have both energy storage and wind plants. In the existing literature, merchants' trading actions are usually assumed not to affect market prices; however, a large-scale energy storage merchant's actions can affect market prices. To this end, we approximate the electricity price by a linear function of the quantity of power traded by the merchant in the reward function to achieve decision-making incorporating the market impact. This paper utilizes the dynamic programming approach to analyze merchants' optimal multi-period decision-making incorporating market impact, uncertain wind generation, and energy storage constraints. First, our results demonstrate that for a merchant with co-located energy storage facilities and wind power plants, the energy storage's feasible state of charge (SOC) range can be segmented into four possible sub-ranges by three analytically developed SOC reference points. The unique optimal trading decision can be achieved by comparing the current energy inventory and the SOC references of the next period. Second, our results show that market impact and uncertainties substantially change the optimal storage scheduling policy by impacting the values of the reference points. To mitigate the negative effect of the merchant's market impact on buying and selling actions, the merchant may reduce the amount of generating or pumping electricity each period to maximize profit. Moreover, we identify and investigate the trade-off between market price and transaction quantity. Our findings provide co-optimized scheduling guidance for electricity merchants with co-located energy storage and renewable power plants systems.

17 WIND ENERGY↗
DOE OSTI2020

A web-based decision support system for collaborative mitigation of multiple water-related hazards using serious gaming

The paper presents a web-based application developed as a medium for executing a serious game to enhance multi-jurisdictional collaborative planning and decision making for mitigation of multiple hazards related to water (i.e., floods, soil erosions, water quality). The gaming application is integrated into the Iowa Watershed Decision Support System (IoWaDSS) designed to serve as a comprehensive multi-sectoral data repository along with the simulation results of process modeling to educate and inform communities on the status of their watersheds during extreme conditions. The interactive serious gaming application was devised to encourage public engagement, facilitate communication and positive relationship between watershed communities, and make the decision process more attractive and transparent for the stakeholders. The web environment offers easily-accessible, user-friendly interfaces that match the skills and knowledge level of a non-technical user. Developed with adaptive design and flexible architecture, the gaming application is generalizable and extendable to support hazard mitigation in other watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

PySIDT: Subgraph Isomorphic Decision Trees for Molecular Property Prediction

Accurate molecular property prediction is important across all fields of chemistry. Deep neural networks (DNNs) have become increasingly popular due to their ability to train automatically, avoiding the incredibly tedious process of constructing and extending traditional property estimation schemes. However, DNNs require large amounts of training data, are challenging to interpret, require large amounts of memory to load even during inference, and have severe difficulties incorporating qualitative chemical knowledge, which are often desired for molecular property prediction tasks. Here, in this study, we present PySIDT (https://github.com/zadorlab/PySIDT), a software for training and running inference on Subgraph Isomorphic Decision Trees (SIDTs). SIDTs are graph-based decision trees made of nodes associated with molecular substructures. Inference is done by descending target molecular structures down the decision tree to nodes with matching subgraph isomorphic substructures and making predictions based on the final (most specific) nodes matched. SIDTs scale down well to dataset sizes much smaller than is feasible for DNNs. As trees of molecular substructures, SIDTs are inherently readable and easy to visualize, making them easy to analyze. They are also straightforward to extend and retrain, facilitate uncertainty estimation, and enable easy integration of expert knowledge. We demonstrate the SIDT approach discussing its application to a diverse range of molecular prediction tasks: rate coefficient estimation, diffusion coefficient estimation, thermochemistry estimation, transition state bond stretch prediction, p K a prediction, stability of molecular structures, stability of surface structures, and prediction of surface lateral interaction energetics. Additionally, we demonstrate the power of the SIDT algorithms in two direct learning curve vanilla comparisons with the popular DNN-based software Chemprop and the popular gradient boosted trees-based software XGBoost on enthalpy of formation and rate coefficient prediction tasks. In particular, in the enthalpy of formation case, vanilla PySIDT is able to outperform vanilla Chemprop and XGBoost across the full range of training/validation set sizes out to 11,560 data points.

Johnson, Matthew Sean [Sandia National Laboratorie↗
DOE OSTI2020

Identifying geologic characteristics and operational decisions to meet global carbon sequestration goals

Geologic carbon sequestration is the process of injecting and storing CO 2 in subsurface reservoirs and is an essential technology for global environmental security (e.g., climate change mitigation) and economic security (e.g., CO 2 tax credits). To meet energy, economic, and environmental goals, society will have to identify vast volumes of high-capacity, low-cost, and viable storage reservoirs for sequestering CO 2 . In turn, this requires understanding how major geologic characteristics (such as reservoir depth, thickness, permeability, porosity, and temperature) and design and operational decisions (such as injection well spacing) impact CO 2 injection rates, storage capacity, and economics. Although many numerical simulation tools exist, they cannot repeat the required thousands or millions of simulations to identify ideal reservoir properties and the sensitivity and interaction between geologic parameters and operational decisions. Here, we use SCO 2 T—a fast-running, reduced-order modeling framework—to explore the sensitivity of major geologic parameters and operational decisions to engineering (CO 2 injection rates, plume dimensions, and storage capacities and effectiveness) and costs. Our results show, for the first time, benefits and impacts such as allowing CO 2 plumes to overlap, how different well spacing patterns affect CO 2 sequestration, the effects on costs of including brine treatment and disposal, and the effect of restricting injection rates to 1 MtCO 2 per y based on well limitations. We reveal multiple novel and unintuitive findings including: (i) deeper reservoirs have reduced carbon sequestration costs until injection rates reach 1 MtCO 2 per y, at which point deeper reservoirs become more expensive, (ii) thicker formations allow for increased injection rates and storage capacity, but thickness barely impacts plume areas, (iii) higher geothermal gradients result in reduced sequestration costs, unless brine treatment/disposal costs are included, at which point reservoirs having lower geothermal gradients are more economical because they produce less brine for each unit of injected CO 2 , and (iv) allowing plumes to overlap has a significantly positive impact of increasing storage capacities but has only a small influence on reducing sequestration costs. Altogether, our results illustrate new scientific conclusions to help identify suitable sites to inject and store CO 2 , to help understand the complex interaction between geology and resulting costs, and to help support the pursuit of meeting global sequestration targets.

58 GEOSCIENCES↗
DOE OSTI2022

Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics

We present a novel application of the machine learning / artificial intelligence method called boosted decision trees to estimate physical quantities on field programmable gate arrays (FPGA). The software package fwXmachina features a new architecture called parallel decision paths that allows for deep decision trees with arbitrary number of input variables. It also features a new optimization scheme to use different numbers of bits for each input variable, which produces optimal physics results and ultraefficient FPGA resource utilization. Problems in high energy physics of proton collisions at the Large Hadron Collider (LHC) are considered. Estimation of missing transverse momentum (E T miss ) at the first level trigger system at the High Luminosity LHC (HL-LHC) experiments, with a simplified detector modeled by Delphes, is used to benchmark and characterize the firmware performance. The firmware implementation with a maximum depth of up to 10 using eight input variables of 16-bit precision gives a latency value of $\mathcal{O}$(10) ns, independent of the clock speed, and $\mathcal{O}$(0.1)% of the available FPGA resources without using digital signal processors.

Instruments & Instrumentation↗
DOE OSTI2025

Real Space Imaging of Field-Driven Decision-Making in Nanomagnetic Galton Boards

A possible spintronic route to hardware implementation for decision-making involves injecting a domain wall into a bifurcated magnetic nanostrip resembling a Y-shaped junction. A decision is made when the domain wall chooses a particular path through the bifurcation. Recently, it was shown that a structure like a nanomagnetic Galton board, which is essentially an array of interconnected Y-shaped junctions, produces outcomes that are stochastic and therefore relevant to artificial neural networks. However, the exact mechanism leading to the robust nature of randomness is unknown. Here, in this study, we directly image the decision-making process in nanomagnetic Galton boards using Lorentz transmission electron microscopy. We identify that the stochasticity in nanomagnetic Galton boards arises as a culmination of (1) the topology of the injected domain wall, (2) dissimilarly sized vertices, and (3) the strength of the applied field. Our results pave the way to a detailed understanding of stochasticity in nanomagnetic networks.

lorentz microscopy↗