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Results for “Physics Informed”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1972

Considerations for an earth physics information-management service.

In a preliminary investigation into the feasibility of establishing a data center for earth physics, 12 disciplines were considered for inclusion. Estimation of the size of the data base for each indicated a need for storage of approximately 10 to the 10th power characters. The computer-based system deemed most worthy of further investigation was the interactive concept with remote-terminal access. Users are divided into three classes according to how they would access information: with no terminal, with an interactive terminal, and with a multidevice terminal. All these users can be served by the same center without any particular difficulty, but the real benefactor is the user with an interactive terminal, because he can compile, debug, and run programs in one continuous session. Final points stressed are multiprogramming for dynamic resource sharing, hardware modularity for future expansion, and information protection for such a large community of users. It is concluded that a survey should be conducted to gather more information from the potential users of such a system, and that a pilot project should be developed at some location where both earth-physics research and data-processing capabilities already exist.

Martin, R. W.↗
NASA NTRSDate not supplied

Hybrid Modeling of Unmanned Aerial Vehicle Electric Powertrain for Fault Detection and Diagnostics

This paper shows the application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived principles and empirical equations, as well as fully connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. It has already been applied to Li-ion batteries in the past, and in this work, we extend the applications to other components of an electric powertrain, namely electronic speed controller with pulse-width modulation, and brushless DC motor with connected propeller. Training and testing of the model is carried out using experimental data from Li-ion battery discharge and powertrain testing in a laboratory environment.

Physics-Informed Machine Learning↗
NASA NTRSDate not supplied

NASA Earth: Synthetic Spectranomics - Deep Learning of Surface 3-D Geometry, Chemistry, and Hyperspectra to Inform Next-generation Land Models

Machine and deep learning (ML/DL) have transformed our approach to Earth observation and system modeling (EOSM), unifying both in view of ML/DL models as a form of data assimilation (DA). Trained on diverse Earth observation records, detailed physical models, or hybrids of both in physics-informed machine learning, ML/DL may improve upon existing Earth system model (ESM) formulations while creating entirely new classes of models. One important application of deep learning is observation synthesis, allowing ESM developers to prepare for the increased spatial, temporal, and spectral/polar resolution of proposed future observing systems. This may involve the retrospective application of learning algorithms to existing observational records, with or without physical radiative transfer models, to co-inform mission planning and ESM development while providing a degree of data continuity for new missions.

Adam Erickson↗
NASA NTRS2003

Development of an Integrated Human Factors Toolkit

An effective integration of human abilities and limitations is crucial to the success of all NASA missions. The Integrated Human Factors Toolkit facilitates this integration by assisting system designers and analysts to select the human factors tools that are most appropriate for the needs of each project. The HF Toolkit contains information about a broad variety of human factors tools addressing human requirements in the physical, information processing and human reliability domains. Analysis of each tool includes consideration of the most appropriate design stage, the amount of expertise in human factors that is required, the amount of experience with the tool and the target job tasks that are needed, and other factors that are critical for successful use of the tool. The benefits of the Toolkit include improved safety, reliability and effectiveness of NASA systems throughout the agency. This report outlines the initial stages of development for the Integrated Human Factors Toolkit.

Resnick, Marc L.↗
NASA NTRS1992

Thermally distinct ejecta blankets from Martian craters

The study of ejecta blankets on Mars gives information about the Martian surface, subsurface, geologic history, atmospheric history, and impact process. In Feb. and Mar. 1989, the Termoskan instrument on board the Phobos 1988 spacecraft of the USSR acquired the highest spatial resolution thermal data ever obtained for Mars, ranging in the resolution from 300 meters to 3 km per pixel. Termoskan simultaneously obtained broad band visible channel data. The data covers a large portion of the equatorial region from 30 degrees S latitude to 6 degrees N latitude. Utilizing the data set we have discovered tens of craters with thermal infrared distinct ejecta (TIDE) in the equatorial regions of Mars. In order to look for correlations within the data, we have compiled a database which currently consists of 110 craters in an area rich in TIDE's and geologic unit variations. For each crater, we include morphologic information from Barlow's Catalog of Large Martian Impact Craters in addition to geographic, geologic, and physical information and Termoskan thermal infrared and visible data.

Betts, B. H.↗
NASA NTRSDate not supplied

Hybrid Modeling for Complex Systems Health Management

The research work presents application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. The powertrain model consists of Li-ion batteries, electronic speed controller with pulse-width modulation, and brush-less DC motor with connected propeller. Results obtained from combination of laboratory and simulation tests are discussed in this work.

PINNS↗
NASA NTRS1995

CNR LARA project, Italy: Airborne laboratory for environmental research

The increasing interest for the environmental problems and the study of the impact on the environment due to antropic activity produced an enhancement of remote sensing applications. The Italian National Research Council (CNR) established a new laboratory for airborne hyperspectral imaging, the LARA Project (Laboratorio Aero per Ricerche Ambientali - Airborne Laboratory for Environmental Research), equipping its airborne laboratory, a CASA-212, mainly with the Daedalus AA5000 MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) instrument. MIVIS's channels, spectral bandwidths, and locations are chosen to meet the needs of scientific research for advanced applications of remote sensing data. MIVIS can make significant contributions to solving problems in many diverse areas such as geologic exploration, land use studies, mineralogy, agricultural crop studies, energy loss analysis, pollution assessment, volcanology, forest fire management and others. The broad spectral range and the many discrete narrow channels of MIVIS provide a fine quantization of spectral information that permits accurate definition of absorption features from a variety of materials, allowing the extraction of chemical and physical information of our environment. The availability of such a hyperspectral imager, that will operate mainly in the Mediterranean area, at the present represents a unique opportunity for those who are involved in environmental studies and land-management to collect systematically large-scale and high spectral-spatial resolution data of this part of the world. Nevertheless, MIVIS deployments will touch other parts of the world, where a major interest from the international scientific community is present.

Bianchi, R.↗
NASA NTRS2015

A Prototype Physical Database for Passive Microwave Retrievals of Precipitation over the US Southern Great Plains

An accurate understanding of the instantaneous, dynamic land surface emissivity is necessary for a physically based, multi-channel passive microwave precipitation retrieval scheme over land. In an effort to assess the feasibility of the physical approach for land surfaces, a semi-empirical emissivity model is applied for calculation of the surface component in a test area of the US Southern Great Plains. A physical emissivity model, using land surface model data as input, is used to calculate emissivity at the 10GHz frequency, combining contributions from the underlying soil and vegetation layers, including the dielectric and roughness effects of each medium. An empirical technique is then applied, based upon a robust set of observed channel covariances, extending the emissivity calculations to all channels. For calculation of the hydrometeor contribution, reflectivity profiles from the Tropical Rainfall Measurement Mission Precipitation Radar (TRMM PR) are utilized along with coincident brightness temperatures (Tbs) from the TRMM Microwave Imager (TMI), and cloud-resolving model profiles. Ice profiles are modified to be consistent with the higher frequency microwave Tbs. Resulting modeled top of the atmosphere Tbs show correlations to observations of 0.9, biases of 1K or less, root-mean-square errors on the order of 5K, and improved agreement over the use of climatological emissivity values. The synthesis of these models and data sets leads to the creation of a simple prototype Tb database that includes both dynamic surface and atmospheric information physically consistent with the land surface model, emissivity model, and atmospheric information.

microwave↗
NASA NTRS2005

An Ensemble Approach to Building Mercer Kernels with Prior Information

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Srivastava, Ashok N.↗