A Method for the Combination of Stochastic Time Varying Load Effects

The problem of evaluating the probability that a structure becomes unsafe under a combination of loads, over a given time period, is addressed. The loads and load effects are modeled as either pulse (static problem) processes with random occurrence time, intensity and a specified shape or intermittent continuous (dynamic problem) processes which are zero mean Gaussian processes superimposed 'on a pulse process. The load coincidence method is extended to problems with both nonlinear limit states and dynamic responses, including the case of correlated dynamic responses. The technique of linearization of a nonlinear limit state commonly used in a time-invariant problem is investigated for timevarying combination problems, with emphasis on selecting the linearization point. Results are compared with other methods, namely the method based on upcrossing rate, simpler combination rules such as Square Root of Sum of Squares and Turkstra's rule. Correlated effects among dynamic loads are examined to see how results differ from correlated static loads and to demonstrate which types of load dependencies are most important, i.e., affect' the exceedance probabilities the most. Application of the load coincidence method to code development is briefly discussed.National Science Foundation Grants CME 79-18053 and CEE 82-0759

Hierarchical search strategy for the detection of gravitational waves from coalescing binaries: Extension to post-Newtonian wave forms

The detection of gravitational waves from coalescing compact binaries would be a computationally intensive process if a single bank of template wave forms (i.e., a one step search) is used. In an earlier paper we had presented a detection strategy, called a two step search}, that utilizes a hierarchy of template banks. It was shown that in the simple case of a family of Newtonian signals, an on-line two step search was about 8 times faster than an on-line one step search (for initial LIGO). In this paper we extend the two step search to the more realistic case of zero spin 1.5 post-Newtonian wave forms. We also present formulas for detection and false alarm probabilities which take statistical correlations into account. We find that for the case of a 1.5 post-Newtonian family of templates and signals, an on-line two step search requires about 1/21 the computing power that would be required for the corresponding on-line one step search. This reduction is achieved when signals having strength S = 10.34 are required to be detected with a probability of 0.95, at an average of one false event per year, and the noise power spectral density used is that of advanced LIGO. For initial LIGO, the reduction achieved in computing power is about 1/27 for S = 9.98 and the same probabilities for detection and false alarm as above.Comment: 30 page RevTeX file and 17 figures (postscript). Submitted to PRD Feb 21, 199

Confidence limits of evolutionary synthesis models. IV Moving forward to a probabilistic formulation

Synthesis models predict the integrated properties of stellar populations. Several problems exist in this field, mostly related to the fact that integrated properties are distributed. To date, this aspect has been either ignored (as in standard synthesis models, which are inherently deterministic) or interpreted phenomenologically (as in Monte Carlo simulations, which describe distributed properties rather than explain them). We approach population synthesis as a problem in probability theory, in which stellar luminosities are random variables extracted from the stellar luminosity distribution function (sLDF). We derive the population LDF (pLDF) for clusters of any size from the sLDF, obtaining the scale relations that link the sLDF to the pLDF. We recover the predictions of standard synthesis models, which are shown to compute the mean of the sLDF. We provide diagnostic diagrams and a simplified recipe for testing the statistical richness of observed clusters, thereby assessing whether standard synthesis models can be safely used or a statistical treatment is mandatory. We also recover the predictions of Monte Carlo simulations, with the additional bonus of being able to interpret them in mathematical and physical terms. We give examples of problems that can be addressed through our probabilistic formalism. Though still under development, ours is a powerful approach to population synthesis. In an era of resolved observations and pipelined analyses of large surveys, this paper is offered as a signpost in the field of stellar populations.Comment: Accepted by A&A. Substantially modified with respect to the 1st draft. 26 pages, 14 fig

Spectral Methods from Tensor Networks

A tensor network is a diagram that specifies a way to "multiply" a collection of tensors together to produce another tensor (or matrix). Many existing algorithms for tensor problems (such as tensor decomposition and tensor PCA), although they are not presented this way, can be viewed as spectral methods on matrices built from simple tensor networks. In this work we leverage the full power of this abstraction to design new algorithms for certain continuous tensor decomposition problems. An important and challenging family of tensor problems comes from orbit recovery, a class of inference problems involving group actions (inspired by applications such as cryo-electron microscopy). Orbit recovery problems over finite groups can often be solved via standard tensor methods. However, for infinite groups, no general algorithms are known. We give a new spectral algorithm based on tensor networks for one such problem: continuous multi-reference alignment over the infinite group SO(2). Our algorithm extends to the more general heterogeneous case.Comment: 30 pages, 8 figure