SEARCH · Engineering Papers

Results for “Deep Operator Networks”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29

All sources NASA NTRS DOE OSTI

NASA NTRS1990

Environmental projects, volume 11. Environmental assessment: Addition to operations building, Mars site

An Environmental Assessment was performed of the proposed addition to building G-86 at the Mars Site, which will provide space for new electronic equipment to consolidate the Deep Space Network (DSN) support facilities from other Goldstone Deep Space Communication Complex (GDSCC) sites at the Mars Site, and will include a fifth telemetry and command group with its associated link monitor, control processor, and operator consoles. The addition of these facilities will increase the capability of the DSN to support future sophisticated NASA spacecraft missions such as the International Solar and Terrestrial Physics (ISTP) Program. The planned construction of this building addition requires an Environmental Assessment (EA) document that records the existing environmental conditions at the Mars Site, that analyzes the environmental effects that possibly could be expected from the construction and use of the new building addition, and that recommends measures to be taken to mitigate any possible deleterious environmental effects.

Source record↗

NASA NTRS2005

Forecasting of Weather Effects for the Deep Space Network

This paper presents a proposed approach for Ka-band link management for deep space applications using daily weather forecasts and discusses the tools that will be employed for operations. Performance metrics are also presented. The proposed approach will be tested in a two-year experiment campaign.

Deep Space Network (DSN)↗

NASA NTRS1976

The deep space network

Progress is reported in flight project support, tracking and data acquisition, research technology, network engineering, hardware and software implementation, and operations.

Source record↗

NASA NTRS1978

The Deep Space Network

Progress in flight project support, tracking and data acquisition (TDA) research and technology, network engineering, hardware and software implementation, and operations are reported.

Source record↗

NASA NTRS2013

Improved Spacecraft Tracking and Navigation Using a Portable Radio Science Receiver

The Portable Radio Science Receiver (PRSR) is a suitcase-sized open-loop digital receiver designed to be small and easy to transport so that it can be deployed quickly and easily anywhere in the world. The PRSR digitizes, downconverts, and filters using custom hardware, firmware, and software. Up to 16 channels can be independently configured and recorded with a total data rate of up to 256 Mbps. The design and implementation of the system's hardware, firmware, and software is described. To minimize costs and time to deployment, our design leveraged elements of the hardware, firmware, and software designs from the existing full-sized operational (non-portable) Radio Science Receivers (RSR) and Wideband VLBI Science Receivers (WVSR), which have successfully supported flagship NASA deep space missions at all Deep Space Network (DSN) sites. We discuss a demonstration of the PRSR using VLBI, with one part per billion angular resolution: 1 nano-radian / 200 ?as synthesized beam. This is the highest resolution astronomical instrument ever operated solely from the Southern Hemisphere. Preliminary results from two sites are presented, including the European Space Agency (ESA) sites at Cebreros, Spain and Malargue, Argentina. Malargue's South American location is of special interest because it greatly improves the geometric coverage for spacecraft navigation in the Southern Hemisphere and will for the first time provide coverage to the 1/4 of the range of declination that has been excluded from reference frame work at Ka-band.

Delta Differential One-Way (DOR)↗

DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Advancing spatiotemporal forecasts of CO 2 plume migration using deep learning networks with transfer learning and interpretation analysis

Accurate and timely forecasts of CO 2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO 2 plumes based on 108 reservoir simulations over a 30-year injection and a 30-year post-injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO 2 plume migration, achieving R 2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient-based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post-injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. In conclusion, the methods proposed in our work enhances the performance and interpretability of CO 2 plume migration forecasts, thereby facilitating informed decision-making throughout the entire lifecycle of GCS applications.

58 GEOSCIENCES↗

DOE OSTI2025

Introduction: Neuromorphic Materials

The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.

Circuits↗

NASA NTRS1972

The deep space network, volume 7

The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Space Flight Operations Facility are described.

Source record↗

NASA NTRS1982

The telecommunications and data acquisition report

Developments in Earth-based radio technology are reported. The Deep Space Network is discussed in terms of its advanced systems, network and facility engineering and implementation, operations, and energy sources. Problems in pulse communication and radio frequency interference are addressed with emphasis on pulse position modulation and laser beam collimation.

Renzetti, N. A.↗

NASA NTRS1991

The Telecommunications and Data Acquisition Report

A compilation is presented of articles on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition. In space communications, radio navigation, radio science, and ground based radio and radar astronomy, activities of the Deep Space Network are reported in planning, in supporting research and technology, in implementation, and in operations. Also included is standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA. In the search for extraterrestrial intelligence (SETI), implementation and operations are reported for searching the microwave spectrum.

Posner, Edward C.↗

NASA NTRS1992

A microwave holography methodology for diagnostics and performance improvement for large reflector antennas

This microwave holography technique utilizes the Fourier transform relation between the complex far field radiation pattern of an antenna and the complex aperture field distribution. Resulting aperture phase and amplitude distribution data can be used to precisely characterize various crucial performance parameters, including panel alignment, panel shaping, subreflector position, antenna aperture illumination, directivity at various frequencies, and gravity deformation effects. The methodology of data processing presented here was successfully applied to the Deep Space Network (DSN) 34-m beam waveguide antennas. The antenna performance was improved at all operating frequencies by reducing the main reflector mechanical surface rms error to 0.43 mm. At Ka-band (32 GHz), the estimated improvement is 4.1 dB, resulting in an aperture efficiency of 52 percent. The performance improvement was verified by efficiency measurements and additional holographic measurements.

Rochblatt, D. J.↗

NASA NTRS1992

Microwave antenna holography

Rochblatt, David J.↗

NASA NTRS2001

DSMS science operations concept

The Deep Space Mission System (DSMS) Science Operations Concept describes the vision for enabling the use of the DSMS, particularly the Deep Space Network (DSN) for direct science observations in the areas of radio astronomy, planetary radar, radio science and VLBI.

deep space science↗

DOE OSTI2022

Soft Actor Critic Based Volt-VAR Co-optimization in Active Distribution Grids

Modern distribution networks are undergoing several technical challenges, such as voltage fluctuations, because of high penetration of distributed energy resources (DERs). This paper proposes a deep reinforcement learning (DRL)-based Volt VAR co-optimization technique for reducing voltage fluctuations as well as power loss under high penetration of DERs. In addition, the proposed approach minimizes the operational cost of the grid. A stochastic policy optimization based soft actor critic (SAC) agent is proposed to configure the optimal set-points of the reactive power outputs of the inverters. The performance of the proposed model is verified on the modified IEEE 34- and 123-bus systems and compared with a base case scenario with no reactive supply by inverters, and a local droop control approach. The results demonstrate that the proposed framework outperforms the conventional droop control method in improving the voltage profile, minimizing the network power loss, and reducing grid operational cost.

—Distribution grids, deep reinforcement learning, ↗

NASA NTRS1982

Telecommunications and data acquisition systems support for the Viking 1975 mission to Mars, volume 5

The support provided by the Deep Space Network to the Viking Project from 1 June 1978 to 30 April 1980 is described. The project was supported by the worldwide network of Deep Space Stations with 26- and 64-meter-diameter antennas, together with a ground communications system, for the transmission of commands, telemetry, radio metric data, and operational instructions between the stations and the network operation control center in Pasadena, California. Assistance was substantially less than in the previous phases of the Viking Project in order to provide adequate support to the Pioneer and Voyager Projects.

Larkin, W. E.↗

NASA NTRS1976

Viking mission support

On April 26, 1976, the network operations control center was declared available for planetary operations in support of the Viking project. This final capability in the network configuration for Viking had been delayed since February with hardware and software problems both in the control center and at the Deep Space stations. The effect of these problems, particularly insofar as they affected production of intermediate data records, the resolution of the problems, and the current status of the network operations control center in the Viking environment, are given in this article. Also discussed are the operational verification tests performed to check out the new capabilities and associated procedures. The Viking project tests supported by the DSN and finally the DSN support of Viking cruise operations, along with some statistics on performance, are included.

Mudgway, D. J.↗

DOE OSTI2022

A Deep Reinforcement Learning-based Reserve Optimization in Active Distribution Systems for Tertiary Frequency Regulation

Federal Energy Regulatory Commission (FERC)Orders 841 and 2222 have recommended that distributed energy resources (DERs) should participate in energy and reserve markets; therefore, a mechanism needs to be developed to facilitate DERs’ participation at the distribution level. Although the available reserve from a single distribution system may not be sufficient for tertiary frequency regulation, stacked and coordinated contributions from several distribution systems can enable them participate in tertiary frequency regulation at scale. This paper proposes a deep reinforcement learning (DRL)-based approach for optimization of requested aggregated reserves by system operators among the clusters of DERs. The co-optimization of cost of reserve, distribution network loss, and voltage regulation of the feeders are considered while optimizing the reserves among participating DERs. The proposed framework adopts deep deterministic policy gradient (DDPG), which is an algorithm based on an actor-critic method. The effectiveness of the proposed method for allocating reserves among DERs is demonstrated through case studies on a modified IEEE 34-node distribution system.

deep reinforcement learning, distributed energy re↗