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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Non-Markovian dynamics of single- and two-qubit systems interacting with Gaussian and non-Gaussian fluctuating transverse environments

We address the interaction of single- and two-qubit systems with an external transverse fluctuating field and analyze in detail the dynamical decoherence induced by Gaussian noise and random telegraph noise (RTN). Upon exploiting the exact RTN solution of the time-dependent von Neumann equation, we analyze in detail the behavior of quantum correlations and prove the non-Markovianity of the dynamical map in the full parameter range, i.e., for either fast or slow noise. The dynamics induced by Gaussian noise is studied numerically and compared to the RTN solution, showing the existence of (state dependent) regions of the parameter space where the two noises lead to very similar dynamics. We show that the effects of RTN noise and of Gaussian noise are different, i.e., the spectrum alone is not enough to summarize the noise effects, but the dynamics under the effect of one kind of noise may be simulated with high fidelity by the other one.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Exact and efficient hybrid Monte Carlo algorithm for accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts

Single cells exhibit a significant amount of variability in transcript levels, which arises from slow, stochastic transitions between gene expression states. Elucidating the nature of these states and understanding how transition rates are affected by different regulatory mechanisms require state-of-the-art methods to infer underlying models of gene expression from single cell data. A Bayesian approach to statistical inference is the most suitable method for model selection and uncertainty quantification of kinetic parameters using small data sets. However, this approach is impractical because current algorithms are too slow to handle typical models of gene expression. To solve this problem, we first show that time-dependent mRNA distributions of discrete-state models of gene expression are dynamic Poisson mixtures, whose mixing kernels are characterized by a piecewise deterministic Markov process. Here, we combined this analytical result with a kinetic Monte Carlo algorithm to create a hybrid numerical method that accelerates the calculation of time-dependent mRNA distributions by 1000-fold compared to current methods. We then integrated the hybrid algorithm into an existing Monte Carlo sampler to estimate the Bayesian posterior distribution of many different, competing models in a reasonable amount of time. We demonstrate that kinetic parameters can be reasonably constrained for modestly sampled data sets if the model is known a priori. If there are many competing models, Bayesian evidence can rigorously quantify the likelihood of a model relative to other models from the data. We demonstrate that Bayesian evidence selects the true model and outperforms approximate metrics typically used for model selection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Emergent facilitation and glassy dynamics in supercooled liquids

In supercooled liquids, dynamical facilitation refers to a phenomenon where microscopic motion begets further motion nearby, resulting in spatially heterogeneous dynamics. This is central to the glassy relaxation dynamics of such liquids, which show super-Arrhenius growth of relaxation timescales with decreasing temperature. Despite the importance of dynamical facilitation, there is no theoretical understanding of how facilitation emerges and impacts relaxation dynamics. Here, we present a theory that explains the microscopic origins of dynamical facilitation. We show that dynamics proceeds by localized bond-exchange events, also known as excitations, resulting in the accumulation of elastic stresses with which new excitations can interact. At low temperatures, these elastic interactions dominate and facilitate the creation of new excitations near prior excitations. Using the theory of linear elasticity and Markov processes, we simulate a model, which reproduces multiple aspects of glassy dynamics observed in experiments and molecular simulations, including the stretched exponential decay of relaxation functions, the super-Arrhenius behavior of relaxation timescales as well as their two-dimensional finite-size effects. The model also predicts the subdiffusive behavior of the mean squared displacement (MSD) on short, intermediate timescales. Furthermore, we derive the phonon contributions to diffusion and relaxation, which when combined with the excitation contributions produce the two-step relaxation processes, and the ballistic–subdiffusive–diffusive crossover MSD behaviors commonly found in supercooled liquids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Collective Dispersal Leads to Variance in Fitness and Maintains Offspring Size Variation within Marine Populations

Variance in fitness is well known to influence the outcome of evolution but is rarely considered in the theory of marine reproductive strategies. In coastal environments, turbulent mesoscale eddies can collect larvae into packets, resulting in collective dispersal. Larvae in packets return to the coast or are lost offshore in groups, producing variance in fitness. Using a Markov process to calculate fixation probabilities for competing phenotypes, we examine the evolution of offspring size and spawning duration in species with benthic adults and pelagic offspring. The offspring size that provides mothers with the highest mean fitness also generates the greatest variance in fitness, but pairwise invasion plots show that bet-hedging strategies are not evolutionarily stable; maximizing expected fitness correctly predicts the unique evolutionarily stable strategy. Nonetheless, fixation can take a long time. We find that selection to increase spawning duration as a risk avoidance strategy to reduce the negative impacts of stochastic recruitment success can allow multiple offspring sizes to coexist in a population for extended periods. This has two important consequences for offspring size: (1) coexistence occurs over a broader range of sizes and is longer when spawning duration is longer because longer spawning durations reduce variation in fitness and increase the time to fixation, and (2) longer spawning durations can compensate for having a nonoptimal size and even allow less optimal sizes to reach fixation. Collective dispersal and longer spawning durations could effectively maintain offspring size variation even in the absence of good and bad years or locations. Finally, empirical comparisons of offspring size would therefore not always reflect environment-specific differences in the optimal size.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

SIMULTANEOUS MULTI-BAND DETECTION OF LOW SURFACE BRIGHTNESS GALAXIES WITH MARKOVIAN MODELING

We present to the astronomical community an algorithm for the detection of low surface brightness (LSB) galaxies in images, called MARSIAA (MARkovian Software for Image Analysis in Astronomy), which is based on multi-scale Markovian modeling. MARSIAA can be applied simultaneously to different bands. It segments an image into a user-defined number of classes, according to their surface brightness and surroundings-typically, one or two classes contain the LSB structures. We have developed an algorithm, called DetectLSB, which allows the efficient identification of LSB galaxies from among the candidate sources selected by MARSIAA. The application of the method to two and three bands simultaneously was tested on simulated images. Based on our tests, we are confident that we can detect LSB galaxies down to a central surface brightness level of only 1.5 times the standard deviation from the mean pixel value in the image background. To assess the robustness of our method, the method was applied to a set of 18 B- and I-band images (covering 1.3 deg{sup 2} in total) of the Virgo Cluster to which Sabatini et al. previously applied a matched-filter dwarf LSB galaxy search algorithm. We have detected all 20 objects from the Sabatini et al. catalog which we could classify by eye as bona fide LSB galaxies. Our method has also detected four additional Virgo Cluster LSB galaxy candidates undetected by Sabatini et al. To further assess the completeness of the results of our method, both MARSIAA, SExtractor, and DetectLSB were applied to search for (1) mock Virgo LSB galaxies inserted into a set of deep Next Generation Virgo Survey (NGVS) gri-band subimages and (2) Virgo LSB galaxies identified by eye in a full set of NGVS square degree gri images. MARSIAA/DetectLSB recovered {approx}20% more mock LSB galaxies and {approx}40% more LSB galaxies identified by eye than SExtractor/DetectLSB. With a 90% fraction of false positives from an entirely unsupervised pipeline, a completeness of 90% is reached for sources with r{sub e} > 3'' at a mean surface brightness level of {mu}{sub g} = 27.7 mag arcsec{sup -2} and a central surface brightness of {mu}{sup 0}{sub g} = 26.7 mag arcsec{sup -2}. About 10% of the false positives are artifacts, the rest being background galaxies. We have found our proposed Markovian LSB galaxy detection method to be complementary to the application of matched filters and an optimized use of SExtractor, and to have the following advantages: it is scale free, can be applied simultaneously to several bands, and is well adapted for crowded regions on the sky.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Phenotypic switching of populations of cells in a stochastic environment

We report that in biology phenotypic switching is a common bet-hedging strategy in the face of uncertain environmental conditions. Existing mathematical models often focus on periodically changing environments to determine the optimal phenotypic response. We focus on the case in which the environment switches randomly between discrete states. Starting from an individual-based model we derive stochastic differential equations to describe the dynamics, and obtain analytical expressions for the mean instantaneous growth rates based on the theory of piecewise-deterministic Markov processes. We show that optimal phenotypic responses are non-trivial for slow and intermediate environmental processes, and systematically compare the cases of periodic and random environments. The best response to random switching is more likely to be heterogeneity than in the case of deterministic periodic environments, net growth rates tend to be higher under stochastic environmental dynamics. The combined system of environment and population of cells can be interpreted as host-pathogen interaction, in which the host tries to choose environmental switching so as to minimise growth of the pathogen, and in which the pathogen employs a phenotypic switching optimised to increase its growth rate. Finally, we discuss the existence of Nash-like mutual best-response scenarios for such host-pathogen games.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Random walks on jammed networks: Spectral properties

Using random walk analyses we explore diffusive transport on networks obtained from contacts between isotropically compressed, monodisperse, frictionless sphere packings generated over a range of pressures in the vicinity of the jamming transition p → 0 . For conductive particles in an insulating medium, conduction is determined by the particle contact network with nodes representing particle centers and edges contacts between particles. The transition rate is not homogeneous, but is distributed inhomogeneously due to the randomness of packing and concomitant disorder of the contact network, e.g., the distribution of the coordination number. A narrow escape time scale is used to write a Markov process for random walks on the particle contact network. This stochastic process is analyzed in terms of spectral density of the random, sparse, Euclidean and real, symmetric, positive, semidefinite transition rate matrix. Findings reveal network structures derived from jammed particles have properties similar to ordered, euclidean lattices but also some unique properties that distinguish them from other structures that are in some sense more homogeneous. Specifically, the distribution of eigenvalues of the transition rate matrix follow a power law with spectral dimension 3. Yet, quantitative details of the statistics of the eigenvectors show subtle differences with homogeneous lattices and allow us to distinguish between topological and geometric sources of disorder in the network.

42 ENGINEERING↗
DOE OSTI2019

Model reduction methods for population dynamics with fast-switching environments: Reduced master equations, stochastic differential equations, and applications

Here we study stochastic population dynamics coupled to fast external environments and combine expansions in the inverse switching rate of the environment and a Kramers–Moyal expansion in the inverse size of the population. This leads to a series of approximation schemes, capturing both intrinsic and environmental noise. These methods provide a means of efficient simulation and we show how they can be used to obtain analytical results for the fluctuations of population dynamics in switching environments. We place the approximations in relation to existing work on piecewise-deterministic and piecewise-diffusive Markov processes. Finally, we demonstrate the accuracy and efficiency of these model-reduction methods in different research fields, including systems in biology and a model of crack propagation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Flow Intermittency, Dispersion, and Correlated Continuous Time Random Walks in Porous Media

We study the intermittency of fluid velocities in porous media and its relation to anomalous dispersion. Lagrangian velocities measured at equidistant points along streamlines are shown to form a spatial Markov process. As a consequence of this remarkable property, the dispersion of fluid particles can be described by a continuous time random walk with correlated temporal increments. This new dynamical picture of intermittency provides a direct link between the microscale flow, its intermittent properties, and non-Fickian dispersion.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2023

Thermalization of Interacting Quasi-One-Dimensional Systems

Many experimentally relevant systems are quasi-one-dimensional, consisting of nearly decoupled chains. In these systems, there is a natural separation of scales between the strong intrachain interactions and the weak interchain coupling. When the intrachain interactions are integrable, weak interchain couplings play a crucial part in thermalizing the system. Here, we develop a Boltzmann-equation formalism involving a collision integral that is asymptotically exact for any interacting integrable system, and apply it to develop a quantitative theory of relaxation in coupled Bose gases in the experimentally relevant Newton's cradle setup. Here we find that relaxation involves a broad spectrum of timescales. We provide evidence that the Markov process governing relaxation at late times is gapless; thus, the approach to equilibrium is generally nonexponential, even for spatially uniform perturbations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Reliability Analysis of Power Grids Considering Component Failures of Variable Energy Resources

This paper proposes an improved model for the reliability assessment of power systems considering component failures of variable energy resources (VER). The inherent intermittency of VER such as solar photovoltaic (PV) and wind farms, along with their susceptibility to component failures, present significant challenges to reliable system operation. These issues, combined with power grid operation and network constraints, complicate the reliable operation of VER-integrated power systems. Here, to address these concerns, this paper introduces a reliability assessment framework that considers VER input variability, its impact on component availability, and their resulting impact on overall system reliability. Stochastic models based on discrete Markov processes are developed to incorporate variable irradiance, wind speeds, and their effects on PV and wind component failure rates. A next-event and state transition-based approach is then developed to integrate the stochastic models into a mixed-timing sequential Monte Carlo simulation framework for composite reliability assessment. Case studies on the RTS-GMLC system demonstrate the effectiveness of the proposed model in evaluating the reliability of VER-integrated systems.

Pandit, Dilip [Sandia National Laboratories (SNL-N↗
DOE OSTI2015

Kullback--Leibler Approximation for Probability Measures on Infinite Dimensional Spaces

In a variety of applications it is important to extract information from a probability measure μ on an infinite dimensional space. Examples include the Bayesian approach to inverse problems and (possibly conditioned) continuous time Markov processes. It may then be of interest to find a measure ν, from within a simple class of measures, which approximates μ. Here, this problem is studied in the case where the Kullback–Leibler divergence is employed to measure the quality of the approximation. A calculus of variations viewpoint is adopted, and the particular case where ν is chosen from the set of Gaussian measures is studied in detail. Basic existence and uniqueness theorems are established, together with properties of minimizing sequences. Furthermore, parameterization of the class of Gaussians through the mean and inverse covariance is introduced, the need for regularization is explained, and a regularized minimization is studied in detail. The calculus of variations framework resulting from this work provides the appropriate underpinning for computational algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

A Stochastic Conceptual Modeling Approach for Examining the Effects of Climate Change on Streamflows in Mountain Basins

Abstract This study presents a modeling approach for examining how changes in climate affect streamflow in mesoscale mountain basins dominated by snowmelt runoff. A conceptual snowmelt-runoff model was developed that is forced by daily time series of temperature and precipitation. The model can be run using either observed climate data or artificial climate data generated from a GCM or a stochastic model. The model was applied to a case-study basin, the north fork of the Clearwater River in Idaho, using stochastically generated climate scenarios. Climate scenarios were generated using a contemporaneous auto-regressive integrated moving average (CARIMA) model for temperature and a precipitation model based on a two-state first-order Markov process. A baseline climate scenario was developed that represents recently observed temperature and precipitation conditions and then 15 additional climate scenarios that represent shifts in recent conditions. For each scenario, model application produced an ensemble of 50 streamflow traces each spanning 30 yr. Results show that an increase in temperature among scenarios leads to a decrease in streamflow and vice versa. Decreases in temperature shift the basin runoff to fully snowmelt dominated, whereas increases in temperature increase the frequency of midwinter runoff events. Increasing precipitation leads to increased runoff in cases where the temperature remains the same as the observed record, but not in cases where the temperature increases. The modeling approach presented here can be used by water managers to examine which types of climate change could require modifications in water planning and operations.

13 HYDRO ENERGY↗
DOE OSTI2014

Large Deviation Methods for the Analysis and Design of Monte Carlo Schemes in Physics and Chemistry. Final Technical Report

This proposal is concerned with applications of Monte Carlo to problems in physics and chemistry where rare events degrade the performance of standard Monte Carlo. One class of problems is concerned with computation of various aspects of the equilibrium behavior of some Markov process via time averages. The problem to be overcome is that rare events interfere with the efficient sampling of all relevant parts of phase space. A second class concerns sampling transitions between two or more stable attractors. Here, rare events do not interfere with the sampling of all relevant parts of phase space, but make Monte Carlo inefficient because of the very large number of samples required to obtain variance comparable to the quantity estimated. The project uses large deviation methods for the mathematical analyses of various Monte Carlo techniques, and in particular for algorithmic analysis and design. This is done in the context of relevant application areas, mainly from chemistry and biology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Realizing the Power of Near-Term Quantum Technologies

This the final report of the LDRD project entitled "Realizing the Power of Near-Term Quantum Technologies", which was tasked with laying a theoretical foundation and computational framework for quantum simulation on quantum devices, to support both future Sandia efforts and the broader academic research effort in this area. The unifying theme of the project has been the desire to delineate more clearly the interface between existent classical computing resources that are vast and reliable with emerging quantum computing resources that will be scarce and unreliable for the foreseeable future. We seek to utilize classical computing resources to judge the efficacy of quantum devices for quantum simulation tasks and determine when they exceed the performance of classical devices, thereby achieving "quantum supremacy". This task was initially pursued by adapting the general concept of "parameter space compression" to quantum simulation. An inability to scale this analysis efficiently to large-scale simulations precipitated a shift in focus to assessing quantum supremacy of a specific quantum device, a 1D Bose gas trapped in an optical lattice, that was more amenable to large-scale analysis. We also seek to reconstruct unobserved information from limited observations of a quantum device to enhance their utility. This task was initially pursued as an application of maximum entropy reconstruction. Initial attempts to improve entropy approximations for direct reconstruction by free energy minimization proved to be more difficult than expected, and the focus shifted to the development of a quantum thermostat to facilitate indirect reconstruction by evolving a quantum Markov process. An efficient quantum thermostat is broadly useful for quantum state preparation in almost any quantum simulation task. In the middle of the project, a small opportunistic investment was made in a high-risk experiment to build an analog quantum simulator out of hole quantum dots in Ge/SiGe heterostructures. While a useful simulator was not produced, hole quantum dots at a Ge/SiGe interface have been successfully observed for the first time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Defender Policy Evaluation and Resource Allocation against MITRE ATT&CK Data and Evaluations

Protecting against multi-step attacks of uncertain duration and timing forces defenders into an indefinite, always ongoing, resource-intensive response. To effectively allocate resources, a defender must be able to analyze multi-step attacks under assumption of constantly allocating resources against an uncertain stream of potentially undetected attacks. To achieve this goal, we present a novel methodology that applies a game-theoretic approach to the attack, attacker, and defender data derived from MITRE´s ATT&CK ® Framework. Time to complete attack steps is drawn from a probability distribution determined by attacker and defender strategies and capabilities. This constraints attack success parameters and enables comparing different defender resource allocation strategies. By approximating attacker-defender games as Markov processes, we represent the attacker-defender interaction, estimate the attack success parameters, determine the effects of attacker and defender strategies, and maximize opportunities for defender strategy improvements against an uncertain stream of attacks. This novel representation and analysis of multi-step attacks enables defender policy optimization and resource allocation, which we illustrate using the data from MITRE´ s APT3 ATT&CK ® Framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Globally optimal interferometry with lossy twin Fock probes

Parity or quadratic spin (e.g., J z 2 ) readouts of a Mach–Zehnder (MZ) interferometer probed with a twin Fock (TF) input state allow saturating the optimal sensitivity attainable among all mode-separable states with a fixed total number of particles but only when the interferometer phase θ is near zero. When more general Dicke state probes are used, the parity readout saturates the quantum Fisher information (QFI) at θ = 0, whereas better-than-standard quantum limit performance of the J z 2 readout is restricted to an o ( N ) occupation imbalance. We show that a method of moments readout of two quadratic spin observables J z 2 and J + 2 + J − 2 is globally optimal for Dicke state probes; i.e., the error saturates the QFI for all θ . In the lossy setting, we derive the time-inhomogeneous Markov process describing the effect of particle loss on TF states, showing that the method of moments readout of four at-most-quadratic spin observables is sufficient for globally optimal estimation of θ when two or more particles are lost. The analysis culminates in a numerical calculation of the QFI matrix for distributed MZ interferometry on the four-mode state | N 4 , N 4 , N 4 , N 4 〉 and its lossy counterparts, showing that an advantage for the estimation of any linear function of the local MZ phases θ 1 and θ 2 (compared to independent probing of the MZ phases by two copies of | N 4 , N 4 〉 ) appears when more than one particle is lost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Potential of Entropic Force in Markov Systems with Nonequilibrium Steady State, Generalized Gibbs Function and Criticality

In this paper we revisit the notion of the “minus logarithm of stationary probability” as a generalized potential in nonequilibrium systems and attempt to illustrate its central role in an axiomatic approach to stochastic nonequilibrium thermodynamics of complex systems. It is demonstrated that this quantity arises naturally through both monotonicity results of Markov processes and as the rate function when a stochastic process approaches a detrministic limit. We then undertake a more detailed mathematical analysis of the consequences of this quantity, culminating in a necessary and sufficient condition for the criticality of stochastic systems. This condition is then discussed in the context of recent results about criticality in biological systems.

entropy↗