Government Contract Award Search - Machine Learning | Federal Compass

Government Contract Award Search - Machine Learning

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80NSSC20K0196 - THE MACHINE LEARNING TECHNIQUE HAS BEEN DEVELOPED TO PREDICT VARIOUS PLASMA QUANTITIES INCLUDING TOTAL PLASMA DENSITY AND PLASMA WAVE AMPLITUDE IN EARTH'S MAGNETOSPHERE USING CONTROLLING INDICES. WE WILL EXTEND THIS TECHNIQUE TO PREDICT THE GLOBAL DISTRIBUTION OF WHISTLER MODE WAVES (CHORUS, HISS, MAGNETOSONIC WAVE), EMIC AND ULF WAVES USING SOLAR WIND PARAMETERS. THE SOLAR WIND PARAMETERS ARE AVAILABLE FROM THE OMNI DATA, AND THE DATASET OF VARIOUS PLASMA WAVES AND ELECTRON DENSITY ARE AVAILABLE FROM THE THEMIS AND VAN ALLEN PROBES MEASUREMENTS. WE WILL USE THE VAN ALLEN PROBES OBSERVATION OF ENERGETIC ELECTRONS TO ANALYZE THE RADIATION BELT ELECTRON FLUX VARIATIONS. AFTER IDENTIFYING TYPICAL EVENTS SHOWING THE ELECTRON RESPONSE TO THE SOLAR WIND DRIVERS, WE WILL SIMULATE THE RADIATION BELT FLUX EVOLUTION USING THE GLOBAL PLASMA WAVE DISTRIBUTION PREDICTED BY THE MACHINE LEARNING TECHNIQUE. THE UCLA 3D DIFFUSION CODE HAS BEEN DEVELOPED AND FULLY VALIDATED FOR SIMULATING THE ELECTRON EVOLUTION DUE TO RESONANT WAVE-PARTICLE INTERACTIONS. FOR COMPARISON, WE WILL ALSO PERFORM THE RADIATION BELT SIMULATION USING THE EMPIRICAL PLASMA WAVE MODEL, WHICH IS AVAILABLE FROM THE RECENT SATELLITE STATISTICS. THE SIMULATION RESULTS WILL BE VALIDATED AGAINST THE SATELLITE OBSERVATIONS USING VALIDATION METRICS.
Grant for Research
Contractor
BOSTON UNIV (TRUSTEES OF BOSTON UNIVERSITY)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
01/31/2020
Obligated Amount
$682.1k
80NSSC20K0201 - RING CURRENT DYNAMICS AND ASSOCIATED WAVE ACTIVITY IN RESPONSE TO SOLAR WIND DRIVERS USING MACHINE LEARNING
Grant for Research
Contractor
University of California (UNIVERSITY OF CALIFORNIA, LOS ANGELES)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
01/28/2020
Obligated Amount
$641.8k
80NSSC20K0298 - Prediction of solar energetic particle radiation timing and dosage using physics-guided machine learning algorithms with remote observations of the solar photosphere, corona and interplanetary medium
Grant for Research
Contractor
FLORIDA INSTITUTE OF TECHNOLOG (FLORIDA INSTITUTE OF TECHNOLOGY, INC.)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
01/15/2020
Obligated Amount
$89.6k
80NSSC20K0290 - Current Understanding: The Sun continuously affects the interplanetary (IP) environment by a host of interconnected physical processes. Coronal Mass Ejections (CMEs), solar flares, and Solar Energetic Particles (SEPs) are key drivers of geomagnetic storms that could cause disruptions of technological systems such as orbiting satellites, global positioning systems, and ground-based power grids, among others. While some CMEs and flares are associated with intense SEPs, some show no or little SEP association. Furthermore, there is no strong connection between the properties of SEPs observed at 1 AU and their progenitors at or near the Sun. This is due to the very complex environment that dominates SEP origin, acceleration, and transport in the IP space. To date, robust long-term (hours) forecasting of SEP properties (e.g., onset, peak intensities, heavy ion composition) does not exist. Goal and Science Questions: Our overarching goal is to determine the feasibility of forecasting SEP occurrence, onset time, peak intensities, and heavy ion properties using data intensive correlative studies and artificial intelligence approaches. We will achieve this goal by answering the following science questions: Q1. What are the relationships between the Sun s photospheric spatial features, erupting regions and flare properties, and the properties of the associated ICMEs, IP shocks, IP plasma, and SEPs measured at 1 AU? Q2. Can this comprehensive set of parameters be combined to predict SEP properties at 1 AU? Data and Methodology: We use X-ray flare properties, photospheric solar images, energetic H-Fe ion, plasma, and magnetic field data from SDO, SOHO, GOES, ACE, Wind, and STEREO-A&B during solar cycles 23 and 24. Using ~6000 catalogued flares during 1998-2018, we will identify those associated with SEP events at 1 AU. For each event and when available, we will derive a matrix of parameters characterizing its associated flare properties, photospheric spatial features, upstream conditions, IP shock, CME, and SEP properties. Statistical and correlation studies will be performed to identify the dominant drivers that affect SEP onset times, peak intensities, and heavy ion properties. Using all inferred parameters, we will then utilize Machine-Learning algorithms to enhance the forecasting of SEP occurrence and physical properties at 1 AU. Expected forecast products, metrics, and validation methods: The study outcome will include the following products: - A comprehensive catalogue of solar flares and their associated photospheric features, CMEs, IP plasma, and SEP properties at 1 AU. - A quantitative assessment of forecasting SEP properties at 1 AU over a time range between ~20 minutes to tens of hours. - Metrics that measure the AI classifiers performances and their accuracies. We will also develop relevant skill scores to asses the goodness of each model predictability power. - Algorithm to validate the models using out-of-time sampling measures. Training and testing data sets will be randomly initialized and will be cross-selected through a large iterative process. Relevance to NASA and H-SWO2R: Our project responds directly to all goals of the Heliophysics Space Weather Operations-to-Research (H-SWO2R) program to Improve forecasts of the energetic proton and/or heavy ion conditions in the heliosphere due to solar eruptions and to improve data utilization techniques that could advance forecasting capabilities and which could also lead to improved scientific understanding. Results are relevant to two science goals of the 2012 Solar and Space Physics Decadal Survey, and to a key strategic goal of NASA s Heliophysics Division, i.e., understand the Sun and its interactions with the Earth and the solar system, including space weather. The outcome also aligns with the interests of NASA Space Radiation Analysis Group (SRAG), NOAA Space Science Prediction Center (SWPC), and the National Space Weather Action Plan (NSWAP).
Grant for Research
Contractor
Southwest Research Institute (SOUTHWEST RESEARCH INSTITUTE)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
01/14/2020
Obligated Amount
$399.4k
80NSSC20K0214 - Exposure to ambient concentrations of ozone (O3) is the second largest pollution-related risk of premature death in the US, leading to approximately 20,000 premature deaths annually. Tens of millions of US citizens live in areas where O3 concentrations exceed federal standards. While air quality forecasts could minimize these exposure risks and help develop attainment strategies, accurate and reliable O3 forecasts have been elusive owing to the computational complexity of O3 air quality modeling. From the prediction of urban-scale pollution distributions, to short-term O3 forecasts, to better understanding the relationship between O3 and climate change, a persistent challenge in the atmospheric community is the computational expense of chemistry within the models used to research these problems. To address these challenges, this project aims at building a robust and computationally efficient chemical DA system, merging research in compressive sampling and machine learning for large- scale dynamical systems. In particular, we will use low-rank tensor representations, demonstrated recently for the first time by Co-PI Doostan for surrogate modeling of chemical kinetics. This approach allows for efficient construction of a surrogate which itself is very computationally cheap to evaluate. Our approach will also draw from expertise in polynomial chaos expansions (PCE a spectral representation of the model solution that can be constructed nonintrusively, i.e., by treating the chemical model as a black box) coupled with multiscale stochastic preconditioners (to address stiffness of the chemical system) to develop fast surrogate models for atmospheric chemistry. In particular, we will accomplish the following objectives: (1) Develop, test, and deliver a surrogate model for the chemical solver in a widely used AQ model (GEOS-Chem). (2) Generalize the surrogate model generation procedure within a software toolbox applicable to any user-provided chemical mechanisms. (3) Demonstrate the benefits of using a surrogate-based AQ modeling framework for assimilation of geostationary observations of atmospheric composition to improve O3 simulations. This project will advance computational tools available for AQ prediction, mitigation, and research. Not only do we aim to deliver a surrogate model for the chemical mechanism of an AQ model used by a very large research community (GEOS-Chem), we will provide a software toolbox for generation of surrogate models for any user-provided mechanism. The potential impacts though are much greater, as improvements in computational expediency could affect research in areas from urban air pollution modeling to longterm studies of chemistry-climate interactions. Eliminating the computational bottleneck associated with chemistry will in turn help to promote the broader use of data assimilation within other AQ forecasting systems. As a case study, we will explore and demonstrate the benefits of surrogate modeling for 4D-Var assimilation using geostationary measurements of NO2, which in the next few years will be available for the first time over North America, Europe, and East Asia. This supports a broader goal of facilitating the use of NASA remote sensing data for air quality forecasting here in the US. The proposed work is highly relevant to the AIST program and broader goals of the earth science and applied sciences programs. Our work directly responds to the NASA AIST proposal solicitation for data-driven modeling tools enabling the forecast of future behavior of the phenomena as well as analytic tools to characterize.
Grant for Research
Contractor
University of Colorado (REGENTS OF THE UNIVERSITY OF COLORADO, THE)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
12/30/2019
Obligated Amount
$150k
80NSSC20K0209 - A core function of the AIST Analytic Center Framework is to facilitate research and analysis that uses the full spectrum of data products available in archives hosting relevant, publicly available data. Key to this is making data FAIR (findable, accessible, interoperable, and reusable), not just for humans, but for automated systems. Effective data discovery services and fully automated, machine-driven transactions require metadata that can be understood by both humans and machines. But such metadata are uncommon. More commonly, metadata records are inadequately contextualized, incomplete, or simply do not exist. When they do exist, they often lack the semantic underpinnings to make them meaningful. The goal of the Automated Metadata Pipeline (AMP) project is to (1) Develop a fully-automated metadata pipeline that integrates machine learning and ontologies to generate syntactically and semantically consistent metadata records that advance FAIR objectives and support Earth science research for a diverse group of stakeholders ranging from scientists to policy makers, and (2) Demonstrate the application of AMP-enhanced data to automate and substantially improve the use and reuse of NASA-hosted data in environmental/Earth systems models in the ARtificial Intelligence for Ecosystem Services (ARIES Villa et al. 2014) platform, a distributed network of ecosystem services and Earth science models and data that relies on semantics to assemble network-available data and model components into ecosystem services models, built on demand and optimized for the context of application. ARIES is a context-aware modeling system that, given adequate, semantically-grounded metadata, is capable of finding and assessing the suitability of candidate data sets for use with a particular model, and automatically establishing linkages between data sources and model components, performing a variety of mediation and pre-processing tasks to integrate heterogeneous data sets. The AMP project aims to use machine learning techniques to auto-generate semantically consistent, variable-level metadata records for NASA data products and, in collaboration with the ARIES developer and user communities, demonstrate their value in supporting scientific research. In so doing, we hope to achieve several objectives: Address usability and scalability issues for data providers and metadata curators in connection with tools for generating robust variable-level metadata records Improve the semantic interoperability of target NASA data products by linking concepts in AMP-generated metadata records to terms from well-established, external vocabularies such as the Environment Ontology (ENVO), thereby taking advantage of existing term mappings between ENVO and ontologies developed by other communities of practice, including NASA s Semantic Web for Earth and Environment Technology (SWEET) Ontology Demonstrate the benefits of semantically interoperable, FAIR data across communities of practice. AMP will provide a platform for auto-generating robust, FAIR-promoting, semantically consistent metadata records using neural nets to assign variables to ontological classes in the AMP Ontology. Our approach recognizes that there is a wealth of information contained within the data itself, which can be exploited to generate accurate and consistent metadata. We will work with the Goddard Space Flight Center s (GSFC) Earth Science (GES) Data and Information Services Center (DISC), which will provide access to data via its OPENDAP servers for training and testing the AMP pipeline, and for use by the ARIES platform. AMP advances the Analytic Center Framework objectives of allowing seamless integration of new and user-supplied components and data increasing research capabilities and speed handling large volumes of data efficiently and providing novel data discovery tools.
Grant for Research
Contractor
LINGUA LOGICA LLC
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
12/10/2019
Obligated Amount
$100k
36C26220C0044 - Geriatric Scholar Coaching
Definitive Contract - 611710 Educational Support Services
Contractor
DZIERZEWSKI, JOSEPH M
Contracting Agency/Office
Veterans Affairs
Effective date
12/09/2019
Obligated Amount
$25k
80NSSC20K0302 - Solar Energetic Particles (SEPs) are among the most hazardous transient phenomena of the solar activity. The primary objective of the proposal is to enhance predictions of SEPs by implementing automatic data characterization and machine-learning tools. The proposal pursuits two main goals: 1) development of the online-accessible automatically-updated database that integrates the solar and heliospheric data, metadata, and descriptors related to Solar Proton Events (SPE) 2) development of robust all-clear forecasts of SPEs with low false-alarm rates, targeted at different temporal scales, different energy and particle flux thresholds of SPEs, and adapted to operational availability of data sources and gaps in the data. We propose to develop databases and tools for quantitative characterization of AR magnetograms and images, estimation of flare eruption probabilities and characterization of SEP events based on machine-learning image analysis and prediction techniques. The proposed database will integrate multi-spacecraft data and modern database technologies with API-based online access to database entries and integration products. It will be made publicly available and serve a foundation for development of machine-learning SPE forecasts. The database will automatically utilize and process incoming observational data, derive additional events descriptors, and will automatically update. The Team will develop high-fidelity all-clear forecasts by employing advanced machine-learning techniques and a customized Skill Score which to optimize tolerance for missed SPE events with respect to the false alarm rate. In addition, the advanced Machine Learning techniques (such as Multi-Layer Perceptrons and Recurrent Neural Networks) will be employed for solving classification tasks and prediction of active region evolution. The proposed research is directly related to the ESI Appendix Topic 4 goals. In particular, the developed database will provide availability and readiness of the prepared and integrated data for the heliophysics community, as well as include automatic detection, measurement, and characterization of SPE-related precursor features. Correspondingly, it will open new perspectives for the data discovery, as well as initiate a data preparation phase for forecasting attempts. The developed robust all-clear forecasts will enable possibilities for safe space exploration and advanced space mission planning, which is especially important with respect to the NASA plan Moon-by-2024 . In longer term prospective, we expect that developed attempts will significantly contribute to the replacement of current SEP operational forecasting techniques by machine learning-based approaches.
Grant for Research
Contractor
NEW JERSEY INST TECHnology (NEW JERSEY INSTITUTE OF TECHNOLOGY)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
12/06/2019
Obligated Amount
$190.4k
FA875020C0539 - SECURE BOOT DEFICIENCIES
Definitive Contract - 541715 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
NTELIGEN, LLC
Contracting Agency/Office
Air Force»Air Force Materiel Command»Air Force Research Laboratory»Sensors Directorate
Effective date
11/06/2019
Obligated Amount
$15k
HR001120C0014 - GUARANTEEING AI ROBUSTNESS AGAINST DECEPTION (GARD) PROGRAM
Definitive Contract - 541715 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
Two Six Labs, LLC (TWO SIX LABS, LLC)
Contracting Agency/Office
Defense»Defense Advanced Research Projects Agency
Effective date
11/04/2019
Obligated Amount
$350k
80NSSC20K0155 - Geospatial Information Tools That Use Machine-Learning to Enable Sustainable Groundwater Management in West Africa
Grant for Research
Contractor
BRIGHAM YOUNG UNIV (BRIGHAM YOUNG UNIVERSITY)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
10/30/2019
Obligated Amount
$214.7k
HR001120C0031 - Competency-Aware Machine Learning (CAML) a Defense Advanced Research Projects Agency program.
Definitive Contract - 541715 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
Charles River Analytics (CHARLES RIVER ANALYTICS, INC.)
Contracting Agency/Office
Defense»Defense Advanced Research Projects Agency
Effective date
10/16/2019
Obligated Amount
$327k
HR001120C0032 - Competency-Aware Machine Learning (CAML) - The purpose of the CAML Program is to improve human-machine teaming capabilities and the task synergies expected of autonomous systems.
Definitive Contract - 541715 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
Draper Labs (CHARLES STARK DRAPER LABORATORY, INC., THE)
Contracting Agency/Office
Defense»Defense Advanced Research Projects Agency
Effective date
10/11/2019
Obligated Amount
$324.9k
HR001120C0033 - Competency-Aware Machine Learning (CAML)- The purpose of the CAML Program is to improve human-machine teaming capabilities and the task synergies expected of autonomous systems.
Definitive Contract - 541715 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
BAE Systems (BAE SYSTEMS INFORMATION AND ELECTRONIC SYSTEMS INTEGRATION INC.)
Contracting Agency/Office
Defense»Defense Advanced Research Projects Agency
Effective date
10/10/2019
Obligated Amount
$406.2k
HR001120C0027 - Competency-Aware Machine Learning a Defense Advanced Research Projects Agency Program.
Definitive Contract - 541715 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
Teledyne (TELEDYNE SCIENTIFIC&IMAGING, LLC)
Contracting Agency/Office
Defense»Defense Advanced Research Projects Agency
Effective date
10/10/2019
Obligated Amount
$407.7k
FA875020C1001 - TIMELY, SECURE, AND MISSION-RESPONSIVE AERIAL WARFIGHTING NETWORK CAPABILITIES
Definitive Contract - 541715 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
Northrop Grumman Corporation (NORTHROP GRUMMAN SYSTEMS CORPORATION)
Contracting Agency/Office
Air Force»Air Force Materiel Command»Air Force Research Laboratory»Sensors Directorate
Effective date
10/07/2019
Obligated Amount
$387.3k
W911NF19C0101 - Novel Artificial Intelligence (AI)/Machine Learning (ML) Test&Evaluation(T&E) and Ensembling
Definitive Contract - 541330 Engineering Services
Contractor
MORSECORP, INC
Contracting Agency/Office
Effective date
09/30/2019
Obligated Amount
$10M
M9549419P0023 - On-Premise AutoML, Base Edition
Purchase Order - 518210 Data Processing, Hosting, and Related Services
Contractor
VERTOSOFT LLC
Contracting Agency/Office
Marine Corps»Deputy Commandant Installations and Logistics»Marine Corps Installations Command (LF/LS)
Effective date
09/30/2019
Obligated Amount
$159.4k
FA810919C0003 - Procurement of Advisory&Assistance Services for Artificial Intelligence&Machine Learning.
Definitive Contract - 541511 Custom Computer Programming Services
Contractor
OPEN SOURCE SYSTEMS, LLC
Contracting Agency/Office
Air Force
Effective date
09/30/2019
Obligated Amount
$2M

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