2,909 research outputs found
Fluid Model Checking of Timed Properties
We address the problem of verifying timed properties of Markovian models of
large populations of interacting agents, modelled as finite state automata. In
particular, we focus on time-bounded properties of (random) individual agents
specified by Deterministic Timed Automata (DTA) endowed with a single clock.
Exploiting ideas from fluid approximation, we estimate the satisfaction
probability of the DTA properties by reducing it to the computation of the
transient probability of a subclass of Time-Inhomogeneous Markov Renewal
Processes with exponentially and deterministically-timed transitions, and a
small state space. For this subclass of models, we show how to derive a set of
Delay Differential Equations (DDE), whose numerical solution provides a fast
and accurate estimate of the satisfaction probability. In the paper, we also
prove the asymptotic convergence of the approach, and exemplify the method on a
simple epidemic spreading model. Finally, we also show how to construct a
system of DDEs to efficiently approximate the average number of agents that
satisfy the DTA specification
Mean-Field approximation and Quasi-Equilibrium reduction of Markov Population Models
Markov Population Model is a commonly used framework to describe stochastic
systems. Their exact analysis is unfeasible in most cases because of the state
space explosion. Approximations are usually sought, often with the goal of
reducing the number of variables. Among them, the mean field limit and the
quasi-equilibrium approximations stand out. We view them as techniques that are
rooted in independent basic principles. At the basis of the mean field limit is
the law of large numbers. The principle of the quasi-equilibrium reduction is
the separation of temporal scales. It is common practice to apply both limits
to an MPM yielding a fully reduced model. Although the two limits should be
viewed as completely independent options, they are applied almost invariably in
a fixed sequence: MF limit first, QE-reduction second. We present a framework
that makes explicit the distinction of the two reductions, and allows an
arbitrary order of their application. By inverting the sequence, we show that
the double limit does not commute in general: the mean field limit of a
time-scale reduced model is not the same as the time-scale reduced limit of a
mean field model. An example is provided to demonstrate this phenomenon.
Sufficient conditions for the two operations to be freely exchangeable are also
provided
NASA SBIR abstracts of 1991 phase 1 projects
The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included
Aeronautical engineering: A continuing bibliography with indexes (supplement 275)
This bibliography lists 379 reports, articles, and other documents introduced into the NASA scientific and technical information system in Jan. 1991
Probabilistic Model Checking for Continuous-Time Markov Chains via Sequential Bayesian Inference
Probabilistic model checking for systems with large or unbounded state space
is a challenging computational problem in formal modelling and its
applications. Numerical algorithms require an explicit representation of the
state space, while statistical approaches require a large number of samples to
estimate the desired properties with high confidence. Here, we show how model
checking of time-bounded path properties can be recast exactly as a Bayesian
inference problem. In this novel formulation the problem can be efficiently
approximated using techniques from machine learning. Our approach is inspired
by a recent result in statistical physics which derived closed form
differential equations for the first-passage time distribution of stochastic
processes. We show on a number of non-trivial case studies that our method
achieves both high accuracy and significant computational gains compared to
statistical model checking
Index to NASA tech briefs, 1971
The entries are listed by category, subject, author, originating source, source number/Tech Brief number, and Tech Brief number/source number. There are 528 entries
Institute for Computational Mechanics in Propulsion (ICOMP)
The Institute for Computational Mechanics in Propulsion (ICOMP) is a combined activity of Case Western Reserve University, Ohio Aerospace Institute (OAI) and NASA Lewis. The purpose of ICOMP is to develop techniques to improve problem solving capabilities in all aspects of computational mechanics related to propulsion. The activities at ICOMP during 1991 are described
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