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NASA NTRSDate not supplied

Uncertainty Quantification using Deep Ensembles for Decision Making in Cyber-Physical-Human Systems

In this paper and its companion, Differential Equation Approximation Using Gradient-Boosted Quantile Regression, Robison et al., we examine an approach to quantifying model uncertainty with the aim of increasing the trustworthiness of computational models in human-machine interactions. In Differential Equation Approximation Using Gradient-Boosted Quantile Regression, we focus on gradient-boosted decision trees, while in this one, we give more details about deep ensembles. Uncertainty quantification is crucial for building trustworthy autonomous decision-making agents in human-machine teams. There are two types of uncertainties: aleatoric and epistemic. The former is related to the inherent stochasticity (noise) of the process, whereas the latter is associated with the lack of knowledge or representation capability of models, such as neural networks. By lack of knowledge, we mean the model’s inability to accurately predict outputs for all possible inputs. The aleatory uncertainty can be estimated fairly easily with, for example, filters, whereas epistemic uncertainty is challenging to compute. This paper uses deep ensembles to quantify both aleatory and epistemic uncertainty. It can act as an uncertainty-aware surrogate transition model for decision-making frameworks. "Uncertainty-aware" means that the surrogate transition model should make predictions along with confidence in those predictions. In the context of decision-making, the transition models are ordinary differential equations (ODEs). Since ODEs can be simulated to make one-step or multi-step predictions, a good surrogate model for them should perform reasonably well in both modes. In a multi-step approach, the trajectory sampling method TS∞ was used to propagate uncertainty over multiple steps. The cartpole dynamical system was selected to demonstrate the ability of deep ensembles as good surrogate transition models for decision-making frameworks. The deep ensembles modeled the dynamics of cartpole ODEs and made uncertainty-aware predictions in single-step and multi-step transition modes.

CPH systems↗
NASA NTRS1990

A coordination theory for intelligent machines

A formal model for the coordination level of intelligent machines is established. The framework of the coordination level investigated consists of one dispatcher and a number of coordinators. The model called coordination structure has been used to describe analytically the information structure and information flow for the coordination activities in the coordination level. Specifically, the coordination structure offers a formalism to (1) describe the task translation of the dispatcher and coordinators; (2) represent the individual process within the dispatcher and coordinators; (3) specify the cooperation and connection among the dispatcher and coordinators; (4) perform the process analysis and evaluation; and (5) provide a control and communication mechanism for the real-time monitor or simulation of the coordination process. A simple procedure for the task scheduling in the coordination structure is presented. The task translation is achieved by a stochastic learning algorithm. The learning process is measured with entropy and its convergence is guaranteed. Finally, a case study of the coordination structure with three coordinators and one dispatcher for a simple intelligent manipulator system illustrates the proposed model and the simulation of the task processes performed on the model verifies the soundness of the theory.

Wang, Fei-Yue↗
NASA NTRS1985

Energy supply processes for magnetospheric substorms and solar flares - Tippy bucket model or pitcher model?

In the past, both magnetospheric substorms and solar flares have almost exclusively been discussed in terms of explosive magnetic reconnection. Such a model may conceptually be illustrated by the so-called 'tippy-bucket model', which causes sudden unloading processes, namely a sudden (catastrophic, stochastic, and unpredictable) conversion of stored magnetic energy. However, recent observations indicate that magnetospheric substorms can be understood as a result of a directly driven process which can conceptually be illustrated by the 'pitcher model' in which the output rate varies in harmony with the input rate. It is also possible that solar flare phenomena are directly driven by a photospheric dynamo. Thus, explosive magnetic reconnection may simply be an unworkable hypothesis and may not be a puzzle to be solved as the primary energy supply process for magnetospheric substorms and solar flares.

Akasofu, S.-I.↗
NASA NTRS1985

Transport of ions in presence of induced electric field and electrostatic turbulence - Source of ions injected into ring current

The transport of ions from the polar ionosphere to the inner magnetosphere during stormtime conditions has been computed using a Monte Carlo diffusion code. The effect of the electrostatic turbulence assumed to be present during the substorm expansion phase was simulated by a process that accelerated the ions stochastically perpendicular to the magnetic field with a diffusion coefficient proportional to the energization rate of the ions by the induced electric field. This diffusion process was continued as the ions were convected from the plasma sheet boundary layer to the double-spiral injection boundary. Inward of the injection boundary, the ions were convected adiabatically. By using as input an O(+) flux of 2.8 x 10 to the 8th per sq cm per s (w greater than 10 eV) and an H(+) flux of 5.5 x 10 to the 8th per sq cm per s (w greater than 0.63 eV), the computed distribution functions of the ions in the ring current were found to be in good agreement, over a wide range in L (4 to 8), with measurements made with the ISEE-1 satellite during a storm. This O(+) flux and a large part of the H(+) flux are consistent with the DE satellite measurements of the polar ionospheric outflow during disturbed times.

Cladis, J. B.↗
NASA NTRS1985

The polar ionosphere as a source of the storm time ring current

The transport of ions from the polar ionosphere to the inner magnetosphere during storm time conditions has been computed using a Monte Carlo diffusion code. The effect of the electrostatic turbulence assumed to be present during the substorm expansion phase was simulated by a process that accelerated the ions stochastically perpendicular to the magnetic field with a diffusion coefficient proportional to the rate of energization of the ions by the induced electric field. This diffusion process was continued as the ions were convected from the plasma sheet boundary layer to the double-spiral injection boundary. Inward of the injection boundary the ions were convected adiabatically. By using as input an O(+) flux of 2.8 x 10 to the 8th per sq cm per s (w greater than 10 eV) and an H(+) flux of 5.5 x 10 to the 8th per sq cm per s (w greater than .63 eV) the computed distribution functions of the ions in the ring current were found to be in good agreement, over a wide range in L (4-8), with measurements made with the ISEE 1 satellite during a storm. This O(+) flux and a large part of the H(+) flux appear to be consistent with the DE-1 and DE-2 satellite measurements of the polar ionospheric outflow during disturbed times.

Cladis, J. B.↗
NASA NTRS1985

On the evaluation of expected performance cost for partially observed closed-loop stochastic systems

New methods are presented for evaluating the expected performance cost of partially observed closed-loop stochastic systems. When the variances of the process statistics are small, a linearized model of the closed-loop stochastic system is defined for which the expected cost can be evaluated by recursion on a set of purely deterministic difference equations. When the variances of the process statistics are large, the linearized model can be used in the control variate method of variance reduction for reducing the number of sample paths required for effective Monte Carlo estimation.

Bayard, D. S.↗
NASA NTRS1975

Computation of output feedback gains for linear stochastic systems using the Zangnill-Powell Method

Because conventional optimal linear regulator theory results in a controller which requires the capability of measuring and/or estimating the entire state vector, it is of interest to consider procedures for computing controls which are restricted to be linear feedback functions of a lower dimensional output vector and which take into account the presence of measurement noise and process uncertainty. To this effect a stochastic linear model has been developed that accounts for process parameter and initial uncertainty, measurement noise, and a restricted number of measurable outputs. Optimization with respect to the corresponding output feedback gains was then performed for both finite and infinite time performance indices without gradient computation by using Zangwill's modification of a procedure originally proposed by Powell. Results using a seventh order process show the proposed procedures to be very effective.

Kaufman, H.↗
NASA NTRS1977

Computation of output feedback gains for linear stochastic systems using the Zangwill-Powell method

Because conventional optimal linear regulator theory results in a controller which requires the capability of measuring and/or estimating the entire state vector, it is of interest to consider procedures for computing controls which are restricted to be linear feedback functions of a lower dimensional output vector and which take into account the presence of measurement noise and process uncertainty. To this effect a stochastic linear model has been developed that accounts for process parameter and initial uncertainty, measurement noise, and a restricted number of measurable outputs. Optimization with respect to the corresponding output feedback gains was then performed for both finite and infinite time performance indices without gradient computation by using Zangwill's modification of a procedure originally proposed by Powell.

Kaufman, H.↗
NASA NTRS1977

Reliability based fatigue design and maintenance procedures

A stochastic model has been developed to describe a probability for fatigue process by assuming a varying hazard rate. This stochastic model can be used to obtain the desired probability of a crack of certain length at a given location after a certain number of cycles or time. Quantitative estimation of the developed model was also discussed. Application of the model to develop a procedure for reliability-based cost-effective fail-safe structural design is presented. This design procedure includes the reliability improvement due to inspection and repair. Methods of obtaining optimum inspection and maintenance schemes are treated.

Hanagud, S.↗
NASA NTRS2005

Development of the Architectural Simulation Model for Future Launch Systems and its Application to an Existing Launch Fleet

A significant portion of lifecycle costs for launch vehicles are generated during the operations phase. Research indicates that operations costs can account for a large percentage of the total life-cycle costs of reusable space transportation systems. These costs are largely determined by decisions made early during conceptual design. Therefore, operational considerations are an important part of vehicle design and concept analysis process that needs to be modeled and studied early in the design phase. However, this is a difficult and challenging task due to uncertainties of operations definitions, the dynamic and combinatorial nature of the processes, and lack of analytical models and the scarcity of historical data during the conceptual design phase. Ultimately, NASA would like to know the best mix of launch vehicle concepts that would meet the missions launch dates at the minimum cost. To answer this question, we first need to develop a model to estimate the total cost, including the operational cost, to accomplish this set of missions. In this project, we have developed and implemented a discrete-event simulation model using ARENA (a simulation modeling environment) to determine this cost assessment. Discrete-event simulation is widely used in modeling complex systems, including transportation systems, due to its flexibility, and ability to capture the dynamics of the system. The simulation model accepts manifest inputs including the set of missions that need to be accomplished over a period of time, the clients (e.g., NASA or DoD) who wish to transport the payload to space, the payload weights, and their destinations (e.g., International Space Station, LEO, or GEO). A user of the simulation model can define an architecture of reusable or expendable launch vehicles to achieve these missions. Launch vehicles may belong to different families where each family may have it own set of resources, processing times, and cost factors. The goal is to capture the required resource levels of the major launch elements and their required facilities. The model s output can show whether or not a certain architecture of vehicles can meet the launch dates, and if not, how much the delay cost would be. It will also produce aggregate figures of missions cost based on element procurement cost, processing cost, cargo integration cost, delay cost, and mission support cost. One of the most useful features of this model is that it is stochastic where it accepts statistical distributions to represent the processing times mimicking the stochastic nature of real systems.

Rabadi, Ghaith↗
NASA NTRS1993

On the origin of planetary spins

We examine the rate of accretion of mass and spin angular momentum by a spherical solid body ('planet') immersed in a differentially rotating disk of particles ('planetesimals'). If the planet travels on a circular orbit, the accretion process is described by two dimensionless parameters r and s, which measure the ratio of the planet's radius to its Hill radius and the ratio of the rms radial velocity of the planetesimals to the shear across the Hill radius, respectively. Using a combination of analytic arguments and numerical simulations, we derive the mass and angular momentum accretion rates and their scaling with r and s. By introducing an additional parameter, the effective mass of the planetesimals relative to the planet, we can derive the obliquities and spins arising from the stochastic nature of the accretion process. Our results are consistent with those of previous calculations by Lissauer and Kary (1991) wherever there is overlap in parameter space. In particular, we conclude that ordered accretion from a uniform or slowly varying disk of small bodies cannot result in rotation as rapid as that of earth or Mars for any value of the disk velocity dispersion s. The spin rates of these planets are most naturally explained as arising from one or a few 'giant' impacts by planetesimals.

Dones, Luke↗
NASA NTRS1998

Secondary Fragmentation of Comet Shoemaker-Levy 9 and the Ramifications for the Progenitor's Breakup in July 1992

Comprehensive analysis of discrete events of secondary fragmentation leads to a conceptually new understanding of the process of disintegration of comet Shoemaker-Levy 9. We submit that the jovian tidal forces inflicted extensive cracks throughout the interior of the original nucleus but did not split it apart. The initial disruption was apparently accomplished by stresses exerted on the cracked object by its fast rotation during the early post-perijove period of time. We argue that this disruption was in fact a rapid sequence of episodes during July 1992 that gave birth to the 12 on-train, or primary, fragments: A, C, D, E, G, H, K, L, Q (later Q(sub 1)), R, S, and W. The discrete events of secondary fragmentation, which gave birth to the off-train fragments, are understood in this scenario as stochastic manifestations of the continuing process of progressive disintegration. Of the 13 off-train fragments considered, nine were secondary--B, F, G(sub 2), M, N, P (later P(sub 2) or P(sub 2a)), Q(sub 2), U, and V--and four tertiary (J, P(sub 1), P(sub 2b), and T). The separation parameters of 11 off-train fragments were determined. The vectorial distribution of separation velocities of these fragments shows a strong concentration toward a great circle, unquestionably an effect of the approximately conserved angular momentum of the progenitor comet since the time of its initial disruption. Also apparent is their clumping (except for P(sub 1)) to a segment along the great circle, implying that the fragments were consistently released from one side of their parents, thus explaining for the first time why the off-train fragments preferentially appeared on one side of the nuclear train. In order to obtain a consistent solution, our model requires that the points of separation be on the antisolar side of the parent fragments, where thermal stresses are likely to enhance the effect of rotation. The episodes of secondary fragmentation are found to have nine months after the close encounter with Jupiter in early July 1992, and the separation velocities ranged between 0.36 and 1.7 m/s. The spin-axis position is determined to have been nearly in the jovicentric orbit plane, which rules out the Asphaug-Benz-Solem strengthless aggregate model as a plausible breakup hypothesis. Since the separation velocities are rotational in nature, they cannot substantially exceed the critical limit for centrifugal breakup and offer an estimate for the original nuclear dimensions. The comet's nucleus is found to have been approximately 10 km in diameter and spinning rapidly. With the exception of P(sub 1) and apparently also P(sub 2) and F, no nongravitational deceleration was detected in the motions of the off-train fragments. Serious doubts are cast on continuing appreciable activity of any of these fragments. Indeed, when it was necessary to introduce a deceleration into the equations of motion, the effect appears to have been due to the action of solar radiation pressure on the centroid of centimeter-sized particulates in the disintegrating condensations.

Sekanina, Z.↗
NASA NTRS1997

Particle Acceleration in Active Galactic Nuclei

The high efficiency of energy generation inferred from radio observations of quasars and X-ray observations of Seyfert active galactic nuclei (AGNs) is apparently achieved only by the gravitational conversion of the rest mass energy of accreting matter onto supermassive black holes. Evidence for the acceleration of particles to high energies by a central engine is also inferred from observations of apparent superluminal motion in flat spectrum, core-dominated radio sources. This phenomenon is widely attributed to the ejection of relativistic bulk plasma from the nuclei of active galaxies, and accounts for the existence of large scale radio jets and lobes at large distances from the central regions of radio galaxies. Reports of radio jets and superluminal motion from galactic black hole candidate X-ray sources indicate that similar processes are operating in these sources. Observations of luminous, rapidly variable high-energy radiation from active galactic nuclei (AGNs) with the Compton Gamma Ray Observatory show directly that particles are accelerated to high energies in a compact environment. The mechanisms which transform the gravitational potential energy of the infalling matter into nonthermal particle energy in galactic black hole candidates and AGNs are not conclusively identified, although several have been proposed. These include direct acceleration by static electric fields (resulting from, for example, magnetic reconnection), shock acceleration, and energy extraction from the rotational energy of Kerr black holes. The dominant acceleration mechanism(s) operating in the black hole environment can only be determined, of course, by a comparison of model predictions with observations. The purpose of the work proposed for this grant was to investigate stochastic particle acceleration through resonant interactions with plasma waves that populate the magnetosphere surrounding an accreting black hole. Stochastic acceleration has been successfully applied to the problem of ion and electron energization in solar flares, and is capable of accounting for a wide range of both neutral and charged particle emissions. It is also a component in diffusive shock acceleration, since pitch-angle scattering (which is necessary for multiple shock crossings) is accompanied by diffusion in momentum space, which in turn yields a net systematic energy gain; however, stochastic energization will dominate the first-order shock process only in certain parameter regimes. Although stochastic acceleration has been applied to particle energization in the lobes of radio galaxies, its application to the central regions of AGNs has only recently been considered, but not in detail. We proposed to systematically investigate the plasma processes responsible for stochastic particle acceleration in black hole magnetospheres along with the energy-loss processes which impede particle energization. To this end we calculated acceleration rates and escape time scales for protons and electrons resonating with Alfven waves, and for electrons resonating with whistlers. Assuming either a Kolmogorov or Kraichnan wave spectrum, accretion at the Eddington limit, magnetic field strengths near equipartition, and turbulence energy densities approx. 10% of the total magnetic field energy density, we find that Alfven waves accelerate protons to Lorentz factors approx, equals 10(exp 4) - 10(exp 6) before they escape from the system. Acceleration of electrons by fast mode and whistler waves can produce a nonthermal population of relativistic electrons whose maximum energy is determined by a competition with radiation losses.

Miller, James A.↗
NASA NTRS2012

The Role of Aerosols on Precipitation Processes: Cloud Resolving Model Simulations

Cloud microphysics is inevitably affected by the smoke particle (CCN, cloud condensation nuclei) size distributions below the clouds. Therefore, size distributions parameterized as spectral bin microphysics are needed to explicitly study the effects of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates for convective clouds. Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. The formulation for the explicit spectral bin microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e. pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), graupel and frozen drops/hail]. Each type is described by a special size distribution function containing many categories (i.e., 33 bins). Atmospheric aerosols are also described using number density size-distribution functions. The model is tested by studying the evolution of deep cloud systems in the west Pacific warm pool region, the sub-tropics (Florida) and midlatitudes using identical thermodynamic conditions but with different concentrations of CCN: a low "clean" concentration and a high "dirty" concentration. Results indicate that the low CCN concentration case produces rainfall at the surface sooner than the high CeN case but has less cloud water mass aloft. Because the spectral-bin model explicitly calculates and allows for the examination of both the mass and number concentration of species in each size category, a detailed analysis of the instantaneous size spectrum can be obtained for these cases. It is shown that since the low (CN case produces fewer droplets, larger sizes develop due to greater condensational and collection growth, leading to a broader size spectrum in comparison to the high CCN case. Sensitivity tests were performed to identify the impact of ice processes, radiation and large-scale influence on cloud-aerosol interactive processes, especially regarding surface rainfall amounts and characteristics (i.e., heavy or convective versus light or stratiform types). In addition, an inert tracer was included to follow the vertical redistribution of aerosols by cloud processes. We will also give a brief review from observational evidence on the role of aerosol on precipitation processes.

Tao, Wei-Kuo↗
NASA NTRS1990

Closed-form solution of decomposable stochastic models

Markov and semi-Markov processes are increasingly being used in the modeling of complex reconfigurable systems (fault tolerant computers). The estimation of the reliability (or some measure of performance) of the system reduces to solving the process for its state probabilities. Such a model may exhibit numerous states and complicated transition distributions, contributing to an expensive and numerically delicate solution procedure. Thus, when a system exhibits a decomposition property, either structurally (autonomous subsystems), or behaviorally (component failure versus reconfiguration), it is desirable to exploit this decomposition in the reliability calculation. In interesting cases there can be failure states which arise from non-failure states of the subsystems. Equations are presented which allow the computation of failure probabilities of the total (combined) model without requiring a complete solution of the combined model. This material is presented within the context of closed-form functional representation of probabilities as utilized in the Symbolic Hierarchical Automated Reliability and Performance Evaluator (SHARPE) tool. The techniques adopted enable one to compute such probability functions for a much wider class of systems at a reduced computational cost. Several examples show how the method is used, especially in enhancing the versatility of the SHARPE tool.

Sjogren, Jon A.↗