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DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗

DOE OSTI2024

D2NO: Efficient handling of heterogeneous input function spaces with distributed deep neural operators

Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems. However, challenges arise when dealing with input functions that exhibit heterogeneous properties, requiring multiple sensors to handle functions with minimal regularity. To address this issue, discretization-invariant neural operators have been used, allowing the sampling of diverse input functions with different sensor locations. However, existing frameworks still require an equal number of sensors for all functions. We propose a novel distributed approach to further relax the discretization requirements and solve the heterogeneous dataset challenges. Our method involves partitioning the input function space and processing individual input functions using independent and separate neural networks. A centralized neural network is used to handle shared information across all output functions. This distributed methodology reduces the number of gradient descent back-propagation steps, improving efficiency while maintaining accuracy. Here, we demonstrate that the corresponding neural network is a universal approximator of continuous nonlinear operators and present three numerical examples to validate its performance.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Real-Time Science Decisioning During High Tempo-High Intensity Mission Operations and the Role of Analogs

Introduction: NASA’s VIPER mission presents a unique operational paradigm within the history of robotic spaceflight. The proximity of the Moon to the Earth and the terrain elements (surface characteristics, light/shadow dynamics, communication links) of the lunar South Polar landing site create unprecedented operational conditions between these two planetary bodies. Apollo era lunar science and exploration included humans in situ to operate instruments and assimilate observational inputs in real-time. Previous lunar orbital missions have worked to operational timescales, e.g., decisional timelines and communication exchanges, that were weeks in length. Mars rover missions have worked to operational timescales, e.g., decisional timelines and communication exchanges between Mars and Earth, that were hours, days, and weeks in length. In the case of the VIPER mission, our operational decisioning for rover driving and instrument commanding will be compressed to minute-scale timeframes. These operational conditions directly impact the manner and speed with which the VIPER Science Team (VST) is required to synthesize and analyze data and produce timely science-driven decisions throughout surface mission operations. The VST shall provide mission enhancing scientific input to guide rover traverse planning and drill site confirmation and selection throughout surface operations. Further, the VST input will be of vital importance to the mission’s ability to maximize science return and to meet broader NASA objectives for future lunar in-situ resource utilization (ISRU)and exploration activities. The VST co-located in the Mission Science Center (MSC) will be responsive to the tactical operational cadence of the Mission Operations Center (MOC) and will provide further strategic and Long-Term Planning (LTP) guidance to the mission. The VIPER Science Operations & Integration(SO&I)team has developed an architecture that is focused on the infusion of science-decisioning into the operational framework and execution cadence of VIPER. NASA analog research has played a significant role in the construction of the VIPER science operations systems. As an example, the SO&I team has led analog missions that have focused on bringing together expertise in the sciences (natural, applied and social) and in operations in service of learning how to build and hold together interdisciplinary work environments and what tools are needed to support high tempo, high intensity integrated decisioning. These experiences have provided an essential foundation of knowledge to the VIPER team. Those analogs that specifically influenced the VIPER science operations construct were identified through a process of comparative analysis to prioritize those that offered relevance in whole or in part, and those that did not. The analog research output that provided extensibility to the VIPER science operations architecture included remote teams of humans and robots in cooperation (synchronous and asynchronous) with simulated earthbound systems, engineering and science teams, and the integrated assembly of tools that supported scientific analysis and data synthesis and provided infrastructure for the remote testing framework. Analogs which included real-time data monitoring, synthesis, visualization and access in a democratized and operationalized manner were of particular interest to the development of the VIPER MSC toolset both in terms of the technology and the processes used to develop the supporting infrastructure. We anticipate that each subsequent mission to the lunar south pole, whether with robots or humans, will be able to optimize science and exploration return by evolving strategies to infuse real-time collaborative science-decisioning. Furthermore, these efforts will result in a foundation for science operations development in support of human-robotic exploration of deep space and Mars. NASA analogs can continue to provide the opportunity to prepare, test and iterate on the operational concepts and tools that will support these ever-expanding space exploration efforts. Our presentation will include an overview of the VIPER Science Operations & Integration development process and specifics on what aspects of analog research have had a significant impact on our work systems.

D S S Lim↗

NASA NTRS2017

Plant Growth Optimization by Vegetable Production System in HI-SEAS Analog Habitat

The Vegetable Production System (Veggie) is a scientific payload designed to support plant growth for food production under microgravity conditions. The configuration of Veggie consists of an LED lighting system with modular rooting pillows designed to contain substrate media and time-release fertilizer. The pillows were designed to be watered passively using capillary principles but have typically been watered manually by the astronauts in low-Earth orbit (LEO). The design of Veggie allows cabin air to be drawn through the plant enclosure for thermal and humidity control and for supplying CO2 to the plants. Since its delivery to the International Space Station (ISS) in 2014, Veggie has undergone several experimental trials by various crews. Ground unit testing of Veggie was conducted during an 8-month Mars analog study in a semi-contained environment of a simulated habitat located at approximately 8,200 feet (2,500 m) elevation on the Mauna Loa volcano on the Island of Hawaii. The Hawaii Space Exploration Analog and Simulation (HI-SEAS) offered conditions (habitat, mission, communications, etc.) intended to simulate a planetary exploration mission. This paper provides data and analyses to show the prospect for optimized use of the current Veggie design for human habitats. Lessons learned during the study may provide opportunities for updating the system design and operational parameters for current Veggie experiments being conducted onboard the ISS and for payloads on future deep space missions.

Ehrlich, Joshua W.↗

NASA NTRS1997

The Evolving Universe: Structure and Evolution of the Universe Roadmap 2000-2020

The Roadmap for the Structure and Evolution of the Universe (SEU) theme embraces three fundamental, scientific quests: (1) To explain structure in the Universe and forecast our cosmic destiny. (2) To explore the cycles of matter and energy in the evolving Universe. (3) To examine the ultimate limits of gravity and energy in the Universe. These quests are developed into six, focused research campaigns addressing the objectives of one or more quests: Identify dark matter and learn how it shapes galaxies and systems of galaxies; Find out where and when the chemical elements were made; Understand the cycles in which matter, energy, and magnetic field are exchanged between stars and the gas between stars; Discover how gas flows in disks and how cosmic jets are formed; Identify the sources of gamma-ray bursts and high-energy cosmic rays; and Measure how strong gravity operates near black holes and how it affects the early Universe. These campaigns lead to a portfolio of future major missions of strong scientific and popular appeal, strongly endorsed by the scientific community and which has undergone significant initial study. Some of these missions are in a state of readiness that makes ideal candidates for the present Office of Space Science Strategic Plan; others may well feature in the next Strategic Plan. Each provides a golden scientific opportunity to advance our understanding of the Universe. Our highest priority science objectives are addressed by five Observatory Class Missions, unranked by science, but in approximate order of readiness: A high-energy gamma-ray facility that will observe relativistic jets and study the sources of cosmic gamma ray bursts; An ultra-sensitive X-ray telescope, optimized for spectroscopy, to examine the hot gas linked with clusters of galaxies, the disks around black holes, and supernova explosions; A large, radio telescope in deep space to map central regions of distant quasars and perform astrometric investigations; An orbiting gravitational coalescing, massive black holes and test how gravity waves distort spacetime; A pair of Earth-orbiting, optical telescopes that will detect flashes of light produced when ultra high-energy cosmic rays impact the upper atmosphere so as to determine their arrival directions and energies. A new program for supporting pertinent international collaboration is strongly endorsed and maintaining a strong Explorer program is important. The flexibility to exploit exceptional opportunities, such as attaching payloads to space station, should also be acquired. A strong technology development program must be initiated now to enable this mission set.

Source record↗

DOE OSTI2021

Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification

Focal Area(s): (2)Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models. (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge- guided AI. Science Challenge: The Energy Exascale Earth System Model (E3SM) is a fully coupled, state-of-the-science Earth system model that uses code optimized for DOE's advanced computers to address the most critical scientific questions facing our nation and society (Golaz et al., 2019). The E3SM Land model (ELM) is designed to understand how the changes in terrestrial land surfaces will interact with other Earth system components and has been used to understand hydrologic cycles, biogeophysics, and ecosystem dynamics. In spite of great successes, the ELM has several known issues that restrain rapid improvements. For example, the ELM uses equilibrium models to simulate dynamic land-climate interactions and it requires long model spin-up time to identify suitable initial conditions for transient simulations. The ELM lacks built-in uncertainty mechanisms that can improve the robustness of model predictions. The ELM is a holistic, deterministic model system with a rigid design, and in many situations, it is hard to modify the ELM system to incorporate new theory/hypothesis and new data across scales to address emerging science problems (such as predicting the impacts of water cycle extremes). In addition, The ELM is technically optimized for traditional CPU-centric computers and it cannot fully utilize the current and incoming leadership computers for model simulations and uncertainty quantification (UQ). The success of artificial intelligence (AI) has inspired scientists to use AI models to discover intrinsic features from simulation data (Chattopadhyay et al., 2020) and observational data (Reichstein et al., 2019) to gain further process understanding of Earth science problems. However, autonomous AI model training through deep learning usually requires a huge amount of annotated data. To overcome the limitations from the data and computing resources, knowledge-guided AI models are necessary where human-knowledge is ingested in model construction (Banino et al., 2018) and training process (Silver et al., 2016) for efficient learning. Herein, we present a new way that leverages the process understanding from the ELM to guide AI model development for the ELM enhancement and UQ. We hope this study can inspire further Earth and environmental system model developments and transformations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2018

Initial Operations Experience and Results from the Juno Gravity Experiment

Radio communications between the Juno spacecraft, in orbit around Jupiter, and the Earth-based observing stations of NASA’s Deep Space Network enable measurements of the Doppler shift induced on the radio signals by Juno’s motion in the Jovian environment. This measurement of the Doppler shift improves the knowledge of Jupiter’s gravitational field. As a radio science instrument, Juno’s gravity science instrument utilizes a ground component at the Deep Space Network’s DSS-25 antenna, equipped with simultaneous dual X- and Ka-band transmitters and receivers, and a spacecraft component, which includes X- and Ka-band transponders to relay the transmitted signal back to Earth. Originally planned to be in 14-day orbits around Jupiter, a risk identified in the propulsion system led to the decision to stay in the 53-day orbit period. Rapid turnaround of observation planning led to successful near-term perijove passes. Although maintaining a 53-day orbit period provides a scientific benefit to the gravity science investigation, the longer orbit period further increases the large dynamic range in Doppler shift and pointing angles induced by the geometry of each perijove. Between entering orbit at Jupiter on July 5, 2016 and September 2017, the Juno spacecraft has executed eight closest approach periods every 53 days where science data was collected. The first five perijove passes were conducted in different telecom configurations, each presenting unique challenges in data collection and processing. Perijoves PJ-01, PJ-02, PJ-03, and PJ-06 utilized the high-gain antenna and various configurations of the X- and Ka-bands. Perijoves PJ-04 and PJ-05 utilized the medium-gain antenna at X-band only while the spacecraft was off-Earth point. Additional perijoves are planned every 53-days, with an additional five by March 2018. Lessons learned from operating and collecting data at each perijove are documented and will be utilized in future perijoves. Analysis of the first two gravity science perijoves has improved the precision of Jupiter’s gravity field by a factor of five, providing crucial constraints on the interior structure of Jupiter.

Buccino, Dustin↗

NASA NTRS2012

An Overview of Suomi NPP VIIRS Calibration Maneuvers

The first Visible Infrared Imager Radiometer Suite (VIIRS) instrument was successfully launched on-board the Suomi National Polar-orbiting Partnership (SNPP) spacecraft on October 28, 2011. Suomi NPP VIIRS observations are made in 22 spectral bands, from the visible (VIS) to the long-wave infrared (LWIR), and are used to produce 22 Environmental Data Records (EDRs) with a broad range of scientific applications. The quality of these VIIRS EDRs strongly depends on the quality of its calibrated and geo-located Sensor Date Records (SDRs). Built with a strong heritage to the NASA's EOS MODerate resolution Imaging Spectroradiometer (MODIS) instrument, the VIIRS is calibrated on-orbit using a similar set of on-board calibrators (OBC), including a solar diffuser (SD) and solar diffuser stability monitor (SDSM) system for the reflective solar bands (RSB) and a blackbody (BB) for the thermal emissive bands (TEB). On-orbit maneuvers of the SNPP spacecraft provide additional calibration and characterization data from the VIIRS instrument which cannot be obtained pre-launch and are required to produce the highest quality SDRs. These include multi-orbit yaw maneuvers for the characterization of SD and SDSM screen transmission, quasi-monthly roll maneuvers to acquire lunar observations to track sensor degradation in the visible through shortwave infrared, and a driven pitch-over maneuver to acquire multiple scans of deep space to determine TEB response versus scan angle (RVS). This paper pro-vides an overview of these three SNPP calibration maneuvers. Discussions are focused on their potential calibration and science benefits, pre-launch planning activities, and on-orbit scheduling and implementation strategies. Results from calibration maneuvers performed during the Intensive Calibration and Validation (ICV) period for the VIIRS sensor are illustrated. Also presented in this paper are lessons learned regarding the implementation of calibration spacecraft maneuvers on follow-on missions.

Butler, James J.↗

DOE OSTI2022

Scalable algorithms for physics-informed neural and graph networks

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

42 ENGINEERING↗

NASA NTRS2014

NASA Space Launch System Operations Outlook

The National Aeronautics and Space Administration's (NASA) Space Launch System (SLS) Program, managed at the Marshall Space Flight Center (MSFC), is working with the Ground Systems Development and Operations (GSDO) Program, based at the Kennedy Space Center (KSC), to deliver a new safe, affordable, and sustainable capability for human and scientific exploration beyond Earth's orbit (BEO). Larger than the Saturn V Moon rocket, SLS will provide 10 percent more thrust at liftoff in its initial 70 metric ton (t) configuration and 20 percent more in its evolved 130-t configuration. The primary mission of the SLS rocket will be to launch astronauts to deep space destinations in the Orion Multi-Purpose Crew Vehicle (MPCV), also in development and managed by the Johnson Space Center. Several high-priority science missions also may benefit from the increased payload volume and reduced trip times offered by this powerful, versatile rocket. Reducing the life-cycle costs for NASA's space transportation flagship will maximize the exploration and scientific discovery returned from the taxpayer's investment. To that end, decisions made during development of SLS and associated systems will impact the nation's space exploration capabilities for decades. This paper will provide an update to the operations strategy presented at SpaceOps 2012. It will focus on: 1) Preparations to streamline the processing flow and infrastructure needed to produce and launch the world's largest rocket (i.e., through incorporation and modification of proven, heritage systems into the vehicle and ground systems); 2) Implementation of a lean approach to reachback support of hardware manufacturing, green-run testing, and launch site processing and activities; and 3) Partnering between the vehicle design and operations communities on state-ofthe- art predictive operations analysis techniques. An example of innovation is testing the integrated vehicle at the processing facility in parallel, rather than sequentially, saving both time and money. These themes are accomplished under the context of a new cross-program integration model that emphasizes peer-to-peer accountability and collaboration towards a common, shared goal. Utilizing the lessons learned through 50 years of human space flight experience, SLS is assigning the right number of people from appropriate backgrounds, providing them the right tools, and exercising the right processes for the job. The result will be a powerful, versatile, and capable heavy-lift, human-rated asset for the future human and scientific exploration of space.

Hefner, William Keith↗

NASA NTRS2014

NASA Space Launch System Operations Outlook

The National Aeronautics and Space Administration's (NASA) Space Launch System (SLS) Program, managed at the Marshall Space Flight Center (MSFC), is working with the Ground Systems Development and Operations (GSDO) Program, based at the Kennedy Space Center (KSC), to deliver a new safe, affordable, and sustainable capability for human and scientific exploration beyond Earth's orbit (BEO). Larger than the Saturn V Moon rocket, SLS will provide 10 percent more thrust at liftoff in its initial 70 metric ton (t) configuration and 20 percent more in its evolved 130-t configuration. The primary mission of the SLS rocket will be to launch astronauts to deep space destinations in the Orion Multi- Purpose Crew Vehicle (MPCV), also in development and managed by the Johnson Space Center. Several high-priority science missions also may benefit from the increased payload volume and reduced trip times offered by this powerful, versatile rocket. Reducing the lifecycle costs for NASA's space transportation flagship will maximize the exploration and scientific discovery returned from the taxpayer's investment. To that end, decisions made during development of SLS and associated systems will impact the nation's space exploration capabilities for decades. This paper will provide an update to the operations strategy presented at SpaceOps 2012. It will focus on: 1) Preparations to streamline the processing flow and infrastructure needed to produce and launch the world's largest rocket (i.e., through incorporation and modification of proven, heritage systems into the vehicle and ground systems); 2) Implementation of a lean approach to reach-back support of hardware manufacturing, green-run testing, and launch site processing and activities; and 3) Partnering between the vehicle design and operations communities on state-of-the-art predictive operations analysis techniques. An example of innovation is testing the integrated vehicle at the processing facility in parallel, rather than sequentially, saving both time and money. These themes are accomplished under the context of a new cross-program integration model that emphasizes peer-to-peer accountability and collaboration towards a common, shared goal. Utilizing the lessons learned through 50 years of human space flight experience, SLS is assigning the right number of people from appropriate backgrounds, providing them the right tools, and exercising the right processes for the job. The result will be a powerful, versatile, and capable heavy-lift, human-rated asset for the future human and scientific exploration of space.

Hefner, William Keith↗

NASA NTRS2018

A Multi-Satellite Assimilation and Modeling Platform to Construct a Global and Complete view of the Hydrologic Cycle

What captivates me about the research field of satellite remote sensing of the earth system science is the opportunity to help human beings by addressing challenging questions related to the current and future availability of natural resources for domestic, agricultural, and industrial needs. For example, a continuously varying climate poses a threat to the water resource, not only in developing countries, but also in economically well-developed regions, creating the urgent need to characterize and predict its spatial and temporal availability. Specifically, my curiosity is driven by the challenge of merging cutting-edge space technology and earth observations (i.e., remote sensing) with state-of-the-art models for the purpose of improving our scientific knowledge about the variability and the change of the hydrologic cycle.The only practical way to observe the land surface processes (e.g., hydrologic cycle) on continental to global scales is via satellite remote sensing. Though remote sensing can make spatially comprehensive measurements of various components of the land surface system, it cannot provide direct information on the entire system, and the measurements represent only a snapshot in time. Physical based models may be used to continuously predict the temporal and spatial earth processes, but these predictions are often poor, due to model initialization, parameter and meteorological forcing errors, and inadequate model physics and/or resolution. Thus, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses of both) to provide a superior land surface state estimate. This is the goal of land surface data assimilation. Data assimilation provides a better estimate of the environmental states than either models or observations could individually do. The broad concept of data assimilation can be applied to various disciplines such as hydrology, ecology, environmental hazards, agriculture and economy.My research is targeted to integrating multiple satellite information having multi-sensor and multi-resolution assimilation within today's state-of-the-art hydrologic models. Multi-sensor and multi-resolution assimilation techniques represent a necessary milestone in future earth science applications because they offer the capability of comprehensively integrating disparate types of earth observations via deep-learning techniques. A multi-satellite assimilation and modeling platform will ultimately provide a robust and complete dataset to more fully understand earth's dynamics. In fact, the multi-satellite assimilation platforms build a comprehensive description of the earth processes that is geared toward accurately representing the complexity of natural and anthropogenic interactions in land surface processes. Most importantly, these platforms are suitable as decision support tools for applications across different aspects of earth system science.My recent work has focused on recent multi-sensor data assimilation techniques targeted at improving snow, soil moisture, groundwater, and terrestrial water storage hydrological states.

Girotto, Manuela↗

NASA NTRSDate not supplied

Unlocking the Mysteries of the Moon’s Shadowed Regions

The Moon poles host large quantities of water-ice deposits in the permanently shadowed regions (PSRs), which are vital for enabling sustainable human space exploration, making these regions high-priority targets for upcoming Artemis missions [1]. Unfortunately, today, the best available orbital lunar imagery [2, 3] lacks the meter-scale resolution and signal needed to understand the geomorphology and trafficability of PSRs, complicating the planning and execution of future missions seeking to explore PSRs. We have developed an image enhancement tool called HORUS (Hyper-effective nOise Removal Unet Software) [4, 5], designed to enhance LRO Narrow-Angle Camera (NAC) optical low-light imagery of permanently shadowed regions by effectively removing the CCD-related, photon, and other residual noises that corrupt the images. The tool is composed of two deep learning neural networks trained on environmental metadata and real and synthetic imagery, the latter generated by a physical noise model (LROC). We demonstrated that HORUS effectively produces low-noise, high-resolution images (~1.5m/px), achieving a 5 to 10x improvement over existing long-exposure images of PSRs. HORUS allows scientists and engineers to identify geomorphic features (e.g., craters and boulders) in shadowed regions as small as 3 meters across as well as to peek inside of small shadowed regions, for the first time. The tool was deployed and thoroughly validated for NASA's VIPER mission [6], where it was applied to 20 candidate target regions across the lunar South Pole. Additionally, we conducted different approaches to validate the resulting HORUS-processed images. With HORUS denoised images, VIPER scientists can increase their confidence on what surface features (previously unseen) exist in the shadowed regions, helping them plan rover traverses more safely and efficiently (e.g., Fig. 1) In this manuscript, we will describe how VIPER scientists are utilizing HORUS denoised images to extract new information from the terrain and increase their confidence in what surface features exist in the shadowed regions. In combination with other high-resolution images and digital elevation maps, HORUS images are helping the team analyze potential lading and science sites, as well as planning traverses more safely and efficiently (e.g., Fig. 1). Additionally, we will describe how HORUS tool unlocks a broad range of scientific and exploration applications to other Artemis and CPLS missions to the lunar poles, including (but not limited to) geomorphic analysis, change detection, surface hazard detection, and terrain relative navigation.

artificial intelligence↗

NASA NTRSDate not supplied

Conclusions of a Mini Technical Interchange Meeting on Mechanisms and Pathways Common Between Adverse Health Outcomes from Exposures to Space Radiation

To enable deep space exploration and sustained human presence in space, the NASA Human Research Program’s (HRP) Space Radiation Element (SRE) funds research to characterize and mitigate adverse health outcomes from exposure to space radiation that include risks of carcinogenesis, cardiovascular disease (CVD) and central nervous system (CNS) decrements. Over the past decade, a growing body of compelling experimental evidence suggests shared mechanisms and pathophysiological processes for CVD, neurodegenerative effects, and cancer development and progression, which are traditionally managed as separate disease processes. Additionally, epidemiological studies have identified cross-sectional and longitudinal associations between some specific types of cancer and CVD, and accumulating evidence indicates that the pathogenesis of neurodegenerative diseases such as Alzheimer’s disease may overlap with CVD and cancer pathogenesis. Identifying the mechanisms and pathways common to these important health decrements will not only accelerate development of effective countermeasures and improve management of spaceflight-induced risk, it will also help to develop new treatment strategies for patients on Earth. To identify common pathways and mechanisms of disease induction and progression from current SR-funded studies and to inform future work and solicitations, the SRE organizes themed sessions at annual HRP Investigators’ Workshops (IWS). These technical interchange meetings (TIMs) provide a venue for the scientific community to present ongoing work and engage in open discussion on results, limitations of current approaches, and incorporation of novel experimental strategies, model systems, and other innovative techniques. Here a summary and lessons learned from the SRE-sponsored mini-TIM titled “mechanisms and pathways common between adverse health outcomes” held at HRP IWS 2023 will be communicated. The 90-min TIM had 30-min dedicated to discussing and developing potential collaboration and tissue sharing opportunities among investigators. The SRE facilitated the discussion using a set of pressing questions and gaps in knowledge that need to be addressed by the scientific community. This poster presents the outcomes of the session along with proposed future workshops and other SRE initiatives.

Janapriya Saha↗

NASA NTRSDate not supplied

Conclusions of a Mini Technical Interchange Meeting on Mechanisms and Pathways Common Between Adverse Health Outcomes from Exposures to Space Radiation

Janapriya Saha↗

DOE OSTI2023

Molecular Simulation of Lithium Carbonate Reactive Vapor–Liquid Equilibria Using a Deep Potential Model

We developed a first-principles machine learning model for the reactive vapor–liquid phase behavior of molten Li 2 CO 3 . The model was trained on ab initio electronic density functional theory data using the Deep Potential (DP) methodology, and its accuracy was evaluated by comparing model predictions of density and viscosity to experimental measurements. Direct coexistence simulations with the DP model over time scales of tens of nanoseconds were used to observe equilibrium dissociation of Li 2 CO 3 into CO 2 residing primarily in the vapor phase and Li 2 O which remains dissolved in the liquid. The simulations covered a range of temperatures, overall system sizes, and vapor-to-liquid volume ratios. Results were analyzed in terms of the observed chemical composition of the liquid and vapor phases, product structure, and CO 2 partial pressures. In addition, we calculated equilibrium constants for the dissociation reaction by assuming ideal-solution behavior for the liquid. As expected on the basis of thermodynamic arguments and prior experiments for this system, the observed partial pressure of CO 2 in the gas phase depends on both the temperature and the ratio of vapor to liquid volumes, while the calculated equilibrium constants only depend on temperature. DP model predictions for the equilibrium constant of the reaction are generally consistent with the available experimental measurements. Furthermore, the present study establishes the validity of the DP methodology for the description of reactive, multiphase equilibria from first principles, with possible applications to many other systems of scientific and technological interest even in the absence of relevant experimental measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

60 APPLIED LIFE SCIENCES↗

NASA NTRSDate not supplied

The Collection, Usage, and Preliminary Examination of the Apollo Sample Suite: Lessons for Artemis

Apollo Sample Collection and Usage: From 1969 to 1972 there were six Apollo missions to the surface of the Moon during which the astronauts collected 382 kg of rock and regolith (~2200 samples). The samples collected fall into these general categories: rocks (~66% by mass), rake samples (~4%), bulk regolith (~24%), and specialty regolith (deep drill cores, drive tubes, sealed bulk regolith) samples (~6%). In each category there are a variety of different subtypes available for study, e.g., among the bulk regolith samples there are also skim, trench, and (partially) shaded regolith samples each sampling unique types or depths of regolith. This variety of subsamples has enabled a multitude of different studies over the past 55 years (>3400 individual requests). We are still averaging ~50 unique requests and have allocated >500 individual Apollo samples annually for the past 10 years (2020 excepted). Looking at the 4,675 non-ANGSA (Apollo Next Generation Sample Analysis) samples allocated over the past 10 years, the proportions of allocated samples do not precisely align with the abundance (by mass) of those samples withing the collection: Rock (69.4 %); Rake (10.8 %); Bulk Regolith (15.5 %); Drive Tube (3.3 %); Core/Specialty (1.0 %). Apollo Preliminary Examination (PE): The PE process differs significantly for the various sample types enumerated above; we focus on regolith and rock samples here. During the Apollo mission era, the PE process evolved over the course of the missions; below is what was done for the Apollo 17 mission. For regolith samples, the PE process was: (1) documented bags containing regolith are opened, photographed, and described; (2) large rocks are removed and treated separately; (3) 25% to 33% of the bulk soil is scooped out, weighed, and stored in reserve; (4) the remaining sample is sieved to produce the size fractions <1, 1- 2, 2-4, and 4-10 mm, all of which are weighed. For rock samples, the process is: (1) removing rocks from the container(s) it came back from the Moon in; (2) rematching any materials that spalled off the rock to their original location; (3) numbering, weighing, and basic photographic documentation; (4) dusting with a gentle N2 gas jet; (5) Orthogonal photography; (6) detailed description of the textures and features of the rock; (7) rock modelling and measurement; (8) stereophotography; (9) determination of the orientation of the rock on the lunar surface. Drive tubes and deep drill cores were not characterized during PE beyond an initial weight and a sketch of the interior tube materials derived from 2D medical X-ray images. Catalogs: The ongoing utility of the Apollo samples is enabled by the robust cataloguing process for the samples [3-5], which allows the scientific community to accurately request samples uniquely suited to their proposed studies. A common misconception, however, is the amount of detail that goes into the initial catalog (e.g., [6]) for a collection from the preliminary examination (PE) period, versus what goes into the catalogs that come later in the life cycle of the samples from that mission (e.g., [7]). The only required data for a PE catalog is a weight, a basic photograph, and a description of the nature of the sample. Artemis PE: Over the past few years, the ongoing ANGSA project studied previously unopened Apollo 17 double drive tube samples 73001/2 [1], and a PE of the drive tubes was done. The PE took the existing core dissection process (developed during PE of Apollo cores in the 1970s, 1980s), and modernized it [7]. The main lesson from the ANGSA PE relative to future missions was that the physical work done during PE of lunar samples has not changed much over the past 5 decades. The use of “modern” technology during PE (e.g., XCT; multispectral analyses) resulted in an enhanced initial understanding of the 73001 and 73002 drive tubes, but greatly increased the time required. The lessons learned from recent astromaterial PEs (e.g., ANGSA and OREx) are important to consider when planning for Artemis, but the unique nature of the Artemis Campaign means many lessons learned from these mission will not be applicable. Given the time constraints (6 months) and likely number of samples that will be returned by Artemis (>200), the Artemis PE catalog will necessarily look much more like [4] than [6].

J Gross↗