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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Information Theory - The Bridge Connecting Bounded Rational Game Theory and Statistical Physics

A long-running difficulty with conventional game theory has been how to modify it to accommodate the bounded rationality of all red-world players. A recurring issue in statistical physics is how best to approximate joint probability distributions with decoupled (and therefore far more tractable) distributions. This paper shows that the same information theoretic mathematical structure, known as Product Distribution (PD) theory, addresses both issues. In this, PD theory not only provides a principle formulation of bounded rationality and a set of new types of mean field theory in statistical physics; it also shows that those topics are fundamentally one and the same.

Wolpert, David H.↗
NASA NTRS2005

Statistical Physics for Adaptive Distributed Control

A viewgraph presentation on statistical physics for distributed adaptive control is shown. The topics include: 1) The Golden Rule; 2) Advantages; 3) Roadmap; 4) What is Distributed Control? 5) Review of Information Theory; 6) Iterative Distributed Control; 7) Minimizing L(q) Via Gradient Descent; and 8) Adaptive Distributed Control.

Wolpert, David H.↗
NASA NTRS1992

Foundations for statistical-physical precipitation retrieval from passive microwave satellite measurements. I - Brightness-temperature properties of a time-dependent cloud-radiation model

The relationship between emerging microwave brightness temperatures (T(B)s) and vertically distributed mixtures of liquid and frozen hydrometeors was investigated, using a cloud-radiation model, in order to establish the framework for a hybrid statistical-physical rainfall retrieval algorithm. Although strong relationships were found between the T(B) values and various rain parameters, these correlations are misleading in that the T(B)s are largely controlled by fluctuations in the ice-particle mixing ratios, which in turn are highly correlated to fluctuations in liquid-particle mixing ratios. However, the empirically based T(B)-rain-rate (T(B)-RR) algorithms can still be used as tools for estimating precipitation if the hydrometeor profiles used for T(B)-RR algorithms are not specified in an ad hoc fashion.

Smith, Eric A.↗
NASA NTRS1999

Statistical physics and physiology: monofractal and multifractal approaches

Even under healthy, basal conditions, physiologic systems show erratic fluctuations resembling those found in dynamical systems driven away from a single equilibrium state. Do such "nonequilibrium" fluctuations simply reflect the fact that physiologic systems are being constantly perturbed by external and intrinsic noise? Or, do these fluctuations actually, contain useful, "hidden" information about the underlying nonequilibrium control mechanisms? We report some recent attempts to understand the dynamics of complex physiologic fluctuations by adapting and extending concepts and methods developed very recently in statistical physics. Specifically, we focus on interbeat interval variability as an important quantity to help elucidate possibly non-homeostatic physiologic variability because (i) the heart rate is under direct neuroautonomic control, (ii) interbeat interval variability is readily measured by noninvasive means, and (iii) analysis of these heart rate dynamics may provide important practical diagnostic and prognostic information not obtainable with current approaches. The analytic tools we discuss may be used on a wider range of physiologic signals. We first review recent progress using two analysis methods--detrended fluctuation analysis and wavelets--sufficient for quantifying monofractual structures. We then describe recent work that quantifies multifractal features of interbeat interval series, and the discovery that the multifractal structure of healthy subjects is different than that of diseased subjects.

NASA Discipline Cardiopulmonary↗
DOE OSTI2024

Cosmic Reionization on Computers: Statistics, Physical Properties, and Environments of Lyman Limit Systems at z ~ 6

Lyman limit systems (LLSs) are dense hydrogen clouds with high enough H i column densities to absorb Lyman continuum photons emitted from distant quasars. Their high column densities imply an origin in dense environments; however, the statistics and distribution of LLSs at high redshifts still remain uncertain. In this paper, we use self-consistent radiative transfer cosmological simulations from the Cosmic Reionization on Computers (CROC) project to study the physical properties of LLSs at the tail end of cosmic reionization at z ~ 6. We generate 3000 synthetic quasar sight lines to obtain a large number of LLS samples in the simulations. In addition, with the high physical fidelity and resolution of CROC, we are able to quantify the association between these LLS samples and nearby galaxies. Our results show that the fraction of LLSs spatially associated with nearby galaxies increases with H i column density. Moreover, we find that LLSs that are not near any galaxy typically reside in filamentary structures connecting neighboring galaxies in the intergalactic medium (IGM). This quantification of the distribution and association of LLSs to large-scale structure informs our understanding of the IGM–galaxy connection during the "Epoch of Reionization," and provides a theoretical basis for interpreting future observations.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2008

Interpolating Fields of Carbon Monoxide Data Using a Hybrid Statistical-Physical Model

Atmospheric Carbon Monoxide (CO) is a pollutant gas of which the US congress has mandated regular monitoring, and satellite sensors can be used to retrieve regional concentrations of CO over several vertical layers. However, CO at cloudy locations cannot be observed and have to be estimated from the observed data set, resulting in an interpolation problem. The current state-of-the-art solution is to combine prior information, computed by a deterministic physical model, with observations. However, the deterministic model may introduce uncertainties that do not derive from the data. While sharing certain features with the physical model, this paper presents a Bayesian hierarchical model for interpolating CO on a 3-dimensional spatial grid, across time. To our knowledge such a model has not been considered before. The model is applied to a hypothetical air-quality monitoring scenario, and is compared to existing interpolation methods. The results provide motivation for the use of the statistical model for regional to local applications.

Arellano, A. A.↗
NASA NTRS1993

Foundations for statistical-physical precipitation retrieval from passive microwave satellite measurements. II - Emission-source and generalized weighting-function properties of a time-dependent cloud-radiation model

The foremost physical aspects of microphysical-radiative interactions that control the formation and modulation of frequency-dependent passive microwave TB over a continental cold-rain precipitation system are explained within the framework of the essential ingredients of a physically based precipitation-retrieval algorithm. The analysis is based on a modeling study in which a 3D cloud model has provided the objective basis for generating an extensive set of microphysical profiles that describe numerous precipitation features in the course of the evolution of a continental hail storm. A summary of the various components of the algorithm, as well as the surface rain rates, is given. The algorithm employs the cloud model to provide a consistent and objectively generated source of detailed microphysical information as the underpinnings to an inversion-based perturbative retrieval scheme.

Mugnai, Alberto↗
DOE OSTI2024

Precision Computations in Strongly Coupled Conformal Field Theories (Final Technical Report)

Conformal Field Theories (CFTs) are quantum field theories that are invariant under the conformal symmetry group (which includes translations and rotations, but also local rescalings of spacetime). They are building blocks of general quantum field theories, and appear in many areas of physics, including statistical physics, condensed matter physics, particle physics, and quantum gravity. Because of their extra symmetries, the mathematical structure of CFTs is tightly constrained, and this leads to the idea of the ``conformal bootstrap," which is to use these mathematical structures to constrain, and in some cases determine, CFT observables. A new numerical implementation of the conformal bootstrap idea appeared in 2008 with the work of Rattazzi, Rychkov, Tonni, and Vichi. Their observation was that certain bootstrap constraints (conformal symmetry and unitarity) could be combined to yield a convex optimization problem that constraints CFT data. By solving this convex optimization problem on a computer, one could obtain bounds on observables like critical exponents and operator product expansion (OPE) coefficients. Over the course of this award, the PI has improved numerical bootstrap techniques by optimizing known algorithms and finding new ones for performing the required convex optimization computations. The PI has applied these techniques to compute high-precision observables in several important strongly-coupled systems. The PI has also explored both analytical and numerical bootstrap methods for constraining the space of low energy effective field theories of quantum gravity, and developed new analytical techniques for CFT and QFT more broadly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2022

Introduction to Analysis Methods for Big Earth Data

Big Earth Data are too big to be tractable to simple data inspection. Thus, they typically require models to make sense of all the data. Useful models for Big Earth Data may be physical, statistical, or machine learning based. While physical models are ideal for understanding the data, they are not always feasible, particularly when our ability to observe at finer scales exceeds our ability to incorporate the physics. Statistical models are more generalized, but computationally intensive for many Earth Observation datasets. Machine Learning models generally scale well but are sometimes limited in the physical understanding they can offer. Hybrid models combine attributes—and advantages—of two or more of these types.

Christopher Lynnes↗
NASA NTRS1990

Statistical and physical evolution of quasi-stellar objects

The relationship between the physical evolution of discrete extragalactic sources, the statistical evolution of the observed population of sources, and the cosmological model are discussed. Three simple forms of statistical evolution - pure luminosity evolution (PLE), pure density evolution (PDE), and generalized luminosity evolution (GLE) are considered in detail together with what these forms imply about the physical evolution of individual sources. Two methods are used to analyze the statistical evolution of the observed distribution of QSOs from combined flux-limited samples. It is shown that both PLE and PDE are inconsistent with the data over the redshift range between 0.2 and 2.2 and that a more complicated form of evolution such as GLE is required, independent of the cosmological model. This result is important for physical models of AGN, and in particular, for the accretion disk model which recent results show may be inconsistent with PLE.

Caditz, David↗
NASA NTRS1989

Statistical and physical evolution of QSO's

The relationship between the physical evolution of discrete extragalactic sources, the statistical evolution of the observed population of sources, and the cosmological model is discussed. Three simple forms of statistical evolution: pure luminosity evolution (PLE), pure density evolution (PDE), and generalized luminosity evolution (GLE), are considered in detail together with what these forms imply about the physical evolution of individual sources. Two methods are used to analyze the statistical evolution of the observed distribution of QSO's (quasars) from combined flux limited samples. It is shown that both PLE and PDE are inconsistent with the data over the redshift range 0 less than z less than 2.2, and that a more complicated form of evolution such as GLE is required, independent of the cosmological model. This result is important for physical models of AGN, and in particular, for the accretion disk model which recent results show may be inconsistent with PLE.

Caditz, David↗
NASA NTRS2001

A Finite-Volume "Shaving" Method for Interfacing NASA/DAO''s Physical Space Statistical Analysis System to the Finite-Volume GCM with a Lagrangian Control-Volume Vertical Coordinate

Toward the development of a finite-volume Data Assimilation System (fvDAS), a consistent finite-volume methodology is developed for interfacing the NASA/DAO's Physical Space Statistical Analysis System (PSAS) to the joint NASA/NCAR finite volume CCM3 (fvCCM3). To take advantage of the Lagrangian control-volume vertical coordinate of the fvCCM3, a novel "shaving" method is applied to the lowest few model layers to reflect the surface pressure changes as implied by the final analysis. Analysis increments (from PSAS) to the upper air variables are then consistently put onto the Lagrangian layers as adjustments to the volume-mean quantities during the analysis cycle. This approach is demonstrated to be superior to the conventional method of using independently computed "tendency terms" for surface pressure and upper air prognostic variables.

Lin, Shian-Jiann↗
NASA NTRS1999

Parallelization of the Physical-Space Statistical Analysis System (PSAS)

Atmospheric data assimilation is a method of combining observations with model forecasts to produce a more accurate description of the atmosphere than the observations or forecast alone can provide. Data assimilation plays an increasingly important role in the study of climate and atmospheric chemistry. The NASA Data Assimilation Office (DAO) has developed the Goddard Earth Observing System Data Assimilation System (GEOS DAS) to create assimilated datasets. The core computational components of the GEOS DAS include the GEOS General Circulation Model (GCM) and the Physical-space Statistical Analysis System (PSAS). The need for timely validation of scientific enhancements to the data assimilation system poses computational demands that are best met by distributed parallel software. PSAS is implemented in Fortran 90 using object-based design principles. The analysis portions of the code solve two equations. The first of these is the "innovation" equation, which is solved on the unstructured observation grid using a preconditioned conjugate gradient (CG) method. The "analysis" equation is a transformation from the observation grid back to a structured grid, and is solved by a direct matrix-vector multiplication. Use of a factored-operator formulation reduces the computational complexity of both the CG solver and the matrix-vector multiplication, rendering the matrix-vector multiplications as a successive product of operators on a vector. Sparsity is introduced to these operators by partitioning the observations using an icosahedral decomposition scheme. PSAS builds a large (approx. 128MB) run-time database of parameters used in the calculation of these operators. Implementing a message passing parallel computing paradigm into an existing yet developing computational system as complex as PSAS is nontrivial. One of the technical challenges is balancing the requirements for computational reproducibility with the need for high performance. The problem of computational reproducibility is well known in the parallel computing community. It is a requirement that the parallel code perform calculations in a fashion that will yield identical results on different configurations of processing elements on the same platform. In some cases this problem can be solved by sacrificing performance. Meeting this requirement and still achieving high performance is very difficult. Topics to be discussed include: current PSAS design and parallelization strategy; reproducibility issues; load balance vs. database memory demands, possible solutions to these problems.

Larson, J. W.↗
NASA NTRS2002

Physical and statistical modeling of Saturn's troposphere

We analyze the 5.2-pm spectra of Saturn by utilizing two independent methods: (a) physical models based on the relevant atmospheric parameters and (b) statistical analysis, based on principal components analysis (PCA), to determine the influence of the variation of phosphine and the opacity of clouds deep within Saturn's atmosphere to understand the dynamics in its atmosphere.

Saturn↗
NASA NTRS2004

Finding Bounded Rational Equilibria: Iterative Focusing - Part 1

A long-running difficulty with conventional game theory has been how to modify it to accommodate the bounded rationality characterizing all real-world players. A recurring issue in statistical physics is how best to approximate joint probability distributions with decoupled (and therefore far more tractable) distributions. It has recently been shown that the same information theoretic mathematical structure, known as Probability Collectives (PC) underlies both issues. This relationship between statistical physics and game theory allows techniques and insights from the one field to be applied to the other. In particular, PC provides a formal model-independent definition of the degree of rationality of a player and of bounded rationality equilibria. This pair of papers extends previous work on PC by introducing new computational approaches to effectively find bounded rationality equilibria of common-interest (team) games.

Wolpert, David H.↗
NASA NTRS2004

Finding Bounded Rational Equilibria: Alternative Lagrangians and Uncountable Move Spaces - Part 2

A long-running difficulty with conventional game theory has been how to modify it to accommodate the bounded rationality characterizing all real-world players. A recurring issue in statistical physics is how best to approximate joint probability distributions with decoupled (and therefore far more tractable) distributions. It has recently been shown that the same information theoretic mathematical structure, known as Probability Collectives (PC) underlies both issues. This relationship between statistical physics and game theory allows techniques and insights &om the one field to be applied to the other. In particular, PC provides a formal model-independent definition of the degree of rationality of a player and of bounded rationality equilibria. This pair of papers extends previous work on PC by introducing new computational approaches to effectively find bounded rationality equilibria of common-interest (team) games.

Wolpert, David H.↗