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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantum Markov chain Monte Carlo with digital dissipative dynamics on quantum computers

Modeling the dynamics of a quantum system connected to the environment is critical for advancing our understanding of complex quantum processes, as most quantum processes in nature are affected by an environment. Modeling a macroscopic environment on a quantum simulator may be achieved by coupling independent ancilla qubits that facilitate energy exchange in an appropriate manner with the system and mimic an environment. This approach requires a large, and possibly exponential number of ancillary degrees of freedom which is impractical. In contrast, we develop a digital quantum algorithm that simulates interaction with an environment using a small number of ancilla qubits. By combining periodic modulation of the ancilla energies, or spectral combing, with periodic reset operations, we are able to mimic interaction with a large environment and generate thermal states of interacting many-body systems. We evaluate the algorithm by simulating preparation of thermal states of the transverse Ising model. Our algorithm can also be viewed as a quantum Markov chain Monte Carlo process that allows sampling of the Gibbs distribution of a multivariate model. To illustrate this we evaluate the accuracy of sampling Gibbs distributions of simple probabilistic graphical models using the algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Bayesian chain graph models to characterize microbe-environment dynamics

Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is problematic given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence, thus agreeing with the experimenter's intuition on the average behavior of nodes under treatment. The solution to our model is sparse via the Bayesian linear regression (LASSO). In addition, we propose an adaptive extension so that different shrinkages can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting, and compositional responses via an appropriate hierarchical structure. We test the performance of our model in a variety of simulated datasets, thereby showing superior performance to state-of-the-art approaches. We further apply our model to human gut and soil microbial compositional datasets, and we highlight that CG-LASSO can estimate biologically meaningful network structures in the data.

compositional data↗
NASA NTRS1992

Performance and state-space analyses of systems using Petri nets

The goal of any modeling methodology is to develop a mathematical description of a system that is accurate in its representation and also permits analysis of structural and/or performance properties. Inherently, trade-offs exist between the level detail in the model and the ease with which analysis can be performed. Petri nets (PN's), a highly graphical modeling methodology for Discrete Event Dynamic Systems, permit representation of shared resources, finite capacities, conflict, synchronization, concurrency, and timing between state changes. By restricting the state transition time delays to the family of exponential density functions, Markov chain analysis of performance problems is possible. One major drawback of PN's is the tendency for the state-space to grow rapidly (exponential complexity) compared to increases in the PN constructs. It is the state space, or the Markov chain obtained from it, that is needed in the solution of many problems. The theory of state-space size estimation for PN's is introduced. The problem of state-space size estimation is defined, its complexities are examined, and estimation algorithms are developed. Both top-down and bottom-up approaches are pursued, and the advantages and disadvantages of each are described. Additionally, the author's research in non-exponential transition modeling for PN's is discussed. An algorithm for approximating non-exponential transitions is developed. Since only basic PN constructs are used in the approximation, theory already developed for PN's remains applicable. Comparison to results from entropy theory show the transition performance is close to the theoretic optimum. Inclusion of non-exponential transition approximations improves performance results at the expense of increased state-space size. The state-space size estimation theory provides insight and algorithms for evaluating this trade-off.

Watson, James Francis, III↗
DOE OSTI2022

Learning Planar Ising Models Software

Learning Planar Ising Models is a software package written in Matlab for learning relationships among variable in a dataset using graphical models. The software package implements a generally-applicable algorithm for learning planar Ising models from any multivariate dataset. The code provides an algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. The software includes demonstrations of the algorithm in simulations and for applications on publicly available datasets. Details of the algorithm, demonstration simulations, and applications are given in Johnson, et al; 2016. Reference: Johnson, J. K., Oyen, D., Chertkov, M., and Netrapalli, P. (2016). Learning planar Ising models. Journal of Machine Learning Research.

Oyen, Diane↗
NASA NTRS1989

The development of the Canadian Mobile Servicing System Kinematic Simulation Facility

Canada will develop a Mobile Servicing System (MSS) as its contribution to the U.S./International Space Station Freedom. Components of the MSS will include a remote manipulator (SSRMS), a Special Purpose Dexterous Manipulator (SPDM), and a mobile base (MRS). In order to support requirements analysis and the evaluation of operational concepts related to the use of the MSS, a graphics based kinematic simulation/human-computer interface facility has been created. The facility consists of the following elements: (1) A two-dimensional graphics editor allowing the rapid development of virtual control stations; (2) Kinematic simulations of the space station remote manipulators (SSRMS and SPDM), and mobile base; and (3) A three-dimensional graphics model of the space station, MSS, orbiter, and payloads. These software elements combined with state of the art computer graphics hardware provide the capability to prototype MSS workstations, evaluate MSS operational capabilities, and investigate the human-computer interface in an interactive simulation environment. The graphics technology involved in the development and use of this facility is described.

Beyer, G.↗
NASA NTRS1994

A Knowledge-Based Representation Scheme for Environmental Science Models

One of the primary methods available for studying environmental phenomena is the construction and analysis of computational models. We have been studying how artificial intelligence techniques can be applied to assist in the development and use of environmental science models within the context of NASA-sponsored activities. We have identified several high-utility areas as potential targets for research and development: model development; data visualization, analysis, and interpretation; model publishing and reuse, training and education; and framing, posing, and answering questions. Central to progress on any of the above areas is a representation for environmental models that contains a great deal more information than is present in a traditional software implementation. In particular, a traditional software implementation is devoid of any semantic information that connects the code with the environmental context that forms the background for the modeling activity. Before we can build AI systems to assist in model development and usage, we must develop a representation for environmental models that adequately describes a model's semantics and explicitly represents the relationship between the code and the modeling task at hand. We have developed one such representation in conjunction with our work on the SIGMA (Scientists' Intelligent Graphical Modeling Assistant) environment. The key feature of the representation is that it provides a semantic grounding for the symbols in a set of modeling equations by linking those symbols to an explicit representation of the underlying environmental scenario.

Keller, Richard M.↗
NASA NTRS1992

HiRel - Reliability/availability integrated workstation tool

The HiRel software tool is described and demonstrated by application to the mission avionics subsystem of the Advanced System Integration Demonstrations (ASID) system that utilizes the PAVE PILLAR approach. HiRel marks another accomplishment toward the goal of producing a totally integrated computer-aided design (CAD) workstation design capability. Since a reliability engineer generally represents a reliability model graphically before it can be solved, the use of a graphical input description language increases productivity and decreases the incidence of error. The graphical postprocessor module HARPO makes it possible for reliability engineers to quickly analyze huge amounts of reliability/availability data to observe trends due to exploratory design changes. The addition of several powerful HARP modeling engines provides the user with a reliability/availability modeling capability for a wide range of system applications all integrated under a common interactive graphical input-output capability.

Bavuso, Salvatore J.↗
NASA NTRS1993

Program Helps In Analysis Of Failures

Failure Environment Analysis Tool (FEAT) computer program developed to enable people to see and better understand effects of failures in system. User selects failures from either engineering schematic diagrams or digraph-model graphics, and effects or potential causes of failures highlighted in color on same schematic-diagram or digraph representation. Uses digraph models to answer two questions: What will happen to system if set of failure events occurs? and What are possible causes of set of selected failures? Helps design reviewers understand exactly what redundancies built into system and where there is need to protect weak parts of system or remove them by redesign. Program also useful in operations, where it helps identify causes of failure after they occur. FEAT reduces costs of evaluation of designs, training, and learning how failures propagate through system. Written using Macintosh Programmers Workshop C v3.1. Can be linked with CLIPS 5.0 (MSC-21927, available from COSMIC).

Stevenson, R. W.↗
NASA NTRS1995

Virtual Reality Calibration and Preview/Predictive Displays for Telerobotics

A virtual reality (VR) calibration technique of matching a virtual environment of simulated 3-D graphic models with actual camera views of the remote site task environment has been developed. This VR calibration enables high-fidelity preview/predictive displays with calibrated graphics overlay on live video. Reliable and accurate calibration is achieved by operator-interactive camera calibration and object localization procedures based on new linear/nonlinear least-squares algorithms that can handle multiple-camera views.

Virtual↗
NASA NTRS1991

Application of enhanced modern structured analysis techniques to Space Station Freedom electric power system requirements

A team of Space Station Freedom (SSF) system engineers are in the process of extensive analysis of the SSF requirements, particularly those pertaining to the electrical power system (EPS). The objective of this analysis is the development of a comprehensive, computer-based requirements model, using an enhanced modern structured analysis methodology (EMSA). Such a model provides a detailed and consistent representation of the system's requirements. The process outlined in the EMSA methodology is unique in that it allows the graphical modeling of real-time system state transitions, as well as functional requirements and data relationships, to be implemented using modern computer-based tools. These tools permit flexible updating and continuous maintenance of the models. Initial findings resulting from the application of EMSA to the EPS have benefited the space station program by linking requirements to design, providing traceability of requirements, identifying discrepancies, and fostering an understanding of the EPS.

Biernacki, John↗
NASA NTRS1994

Virtual Reality Calibration for Telerobotic Servicing

A virtual reality calibration technique of matching a virtual environment of simulated graphics models in 3-D geometry and perspective with actual camera views of the remote site task environment has been developed to enable high-fidelity preview/predictive displays with calibrated graphics overlay on live video.

error measurements↗
NASA NTRS1993

Engineering workstation: Sensor modeling

The purpose of the engineering workstation is to provide an environment for rapid prototyping and evaluation of fusion and image processing algorithms. Ideally, the algorithms are designed to optimize the extraction of information that is useful to a pilot for all phases of flight operations. Successful design of effective fusion algorithms depends on the ability to characterize both the information available from the sensors and the information useful to a pilot. The workstation is comprised of subsystems for simulation of sensor-generated images, image processing, image enhancement, and fusion algorithms. As such, the workstation can be used to implement and evaluate both short-term solutions and long-term solutions. The short-term solutions are being developed to enhance a pilot's situational awareness by providing information in addition to his direct vision. The long term solutions are aimed at the development of complete synthetic vision systems. One of the important functions of the engineering workstation is to simulate the images that would be generated by the sensors. The simulation system is designed to use the graphics modeling and rendering capabilities of various workstations manufactured by Silicon Graphics Inc. The workstation simulates various aspects of the sensor-generated images arising from phenomenology of the sensors. In addition, the workstation can be used to simulate a variety of impairments due to mechanical limitations of the sensor placement and due to the motion of the airplane. Although the simulation is currently not performed in real-time, sequences of individual frames can be processed, stored, and recorded in a video format. In that way, it is possible to examine the appearance of different dynamic sensor-generated and fused images.

Pavel, M↗
DOE OSTI2022

A Review of Bayesian Networks for Spatial Data

We report Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Dynamic Scheduling Under Uncertainty

Often, autonomous agents that operate in real-world environments must be able to schedule and execute actions to accomplish mission goals while being robust to uncertainty and disturbances that arise during operations. This tutorial will focus on classes of models and algorithms that have been used to enable effective, robust strategies for dynamically dispatching plans in real time. The tutorial will begin with a survey of past and current research in modeling uncertainty in planning and scheduling, as well as research in different classes of approaches to dynamic scheduling. We then focus on a general class of approaches based on executing temporal plans using graphical models of events and their temporal ordering. These approaches allow for ensuring temporal flexibility in scheduling, as well as for developing strategies for executing partially controllable plans.

Morris, Robert↗
DOE OSTI2023

A Bayesian model for multivariate discrete data using spatial and expert information with application to inferring building attributes

When modeling sparsely observed multivariate data, strong prior information elicited from experts can be used to bolster predictive accuracy and counteract sampling bias. Similarly, modeling autocorrelation in space can help make use of co-occurrence patterns present in many types of spatial data. To make use of both expert prior information and spatial structure, we propose a novel graphical model for a spatial Bayesian network developed specifically to address challenges in inferring the attributes of buildings from geographically sparse observational data. This model is implemented as the sum of a spatial multivariate Gaussian random field and a tabular conditional probability function in real-valued space prior to projection onto the probability simplex. This modeling form is especially suitable for the usage of prior information in the form of sets of atomic rules obtained from experts. To perform inference with missing data, we implement a Markov chain Monte Carlo scheme composed of alternating steps of Gibbs sampling of missing entries and Hamiltonian Monte Carlo for model parameters. A case study in building attribution is presented to highlight the advantages and limitations of this approach.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

Modelling of DNA-protein recognition

Computer model-building procedures using stereochemical principles together with theoretical energy calculations appear to be, at this stage, the most promising route toward the elucidation of DNA-protein binding schemes and recognition principles. A review of models and bonding principles is conducted and approaches to modeling are considered, taking into account possible di-hydrogen-bonding schemes between a peptide and a base (or a base pair) of a double-stranded nucleic acid in the major groove, aspects of computer graphic modeling, and a search for isogeometric helices. The energetics of recognition complexes is discussed and several models for peptide DNA recognition are presented.

Rein, R.↗
NASA NTRS2002

An Evaluation of the ROSE System

A request-oriented scheduling engine, better known as ROSE, is under development within the Flight Projects Directorate for the purpose of planning and scheduling of the activities and resources associated with the science experiments to be performed aboard the International Space Station (ISS). ROSE is being designed to incrementally process requests from payload developers (PDs) to model and schedule the execution of their science experiments on the ISS. The novelty of the approach comes from its web-based interface permitting the PDs to define their request via the construction of a graphical model to represent their requirements. Based on an examination of the current ROSE implementation, this paper proposes several recommendations for changes to the modeling component and makes mention of other potential applications of the ROSE system.

John M. Usher↗