Stochastic Optimal Prediction with Application to Averaged Euler Equations

Optimal prediction (OP) methods compensate for a lack of resolution in the numerical solution of complex problems through the use of an invariant measure as a prior measure in the Bayesian sense. In first-order OP, unresolved information is approximated by its conditional expectation with respect to the invariant measure. In higher-order OP, unresolved information is approximated by a stochastic estimator, leading to a system of random or stochastic differential equations. We explain the ideas through a simple example, and then apply them to the solution of Averaged Euler equations in two space dimensions.Comment: 13 pages, 2 figure

Introduction to focus issue: intrinsic and designed computation: information processing in dynamical systems-beyond the digital hegemony

How dynamical systems store and process information is a fundamental question that touches a remarkably wide set of contemporary issues: from the breakdown of Moore's scaling laws-that predicted the inexorable improvement in digital circuitry-to basic philosophical problems of pattern in the natural world. It is a question that also returns one to the earliest days of the foundations of dynamical systems theory, probability theory, mathematical logic, communication theory, and theoretical computer science. We introduce the broad and rather eclectic set of articles in this Focus Issue that highlights a range of current challenges in computing and dynamical systems

Ethical Allocation of Remdesivir

As the federal government distributed remdesivir to some of the states COVID-19 hit hardest, policymakers scrambled to develop criteria to allocate the drug to their hospitals. Our state, Michigan, was among those states to receive an initial quantity of the drug from the U.S. government. The disparities in burden of disease in Michigan are striking. Detroit has a death rate more than three times the state average. Our recommendation to the state was that it should prioritize the communities that bear a disproportionate burden of suffering in the allocation of the new potential treatment. This recommendation is justified not only for new drugs with uncertain effects, but also for drugs of certain benefit or vaccines. For states with significant health disparities, such as Michigan, this allocation priority may help to repair them. In fact, any other allocation strategy may make them wors

An Adaptive Semi-Implicit Scheme for Simulations of Unsteady Viscous Compressible Flows

A numerical scheme for simulation of unsteady, viscous, compressible flows is considered. The scheme employs an explicit discretization of the inviscid terms of the Navier-Stokes equations and an implicit discretization of the viscous terms. The discretization is second order accurate in both space and time. Under appropriate assumptions, the implicit system of equations can be decoupled into two linear systems of reduced rank. These are solved efficiently using a Gauss-Seidel method with multigrid convergence acceleration. When coupled with a solution-adaptive mesh refinement technique, the hybrid explicit-implicit scheme provides an effective methodology for accurate simulations of unsteady viscous flows. The methodology is demonstrated for both body-fitted structured grids and for rectangular (Cartesian) grids

The Impact of Advocacy Organizations on Low-Income Housing Policy in U.S. Cities

Financial support for affordable housing competes with many other municipal priorities. This work seeks to explain the variation in support for affordable housing among U.S. cities with populations of 100,000 or more. Using multivariate statistical analysis, this research investigates political explanations for the level of city expenditures on housing and community with a particular interest in the influence of housing advocacy organizations (AOs). Data for the model were gathered from secondary sources, including the U.S. Census and the National Center for Charitable Statistics. Among other results, the analysis indicates that, on average, the political maturity of AOs has a statistically significant, positive effect on local housing and community development expenditures

Stochastic Optimal Prediction with Application to Averaged Euler Equations

Optimal prediction (OP) methods compensate for a lack of resolution in the numerical solution of complex problems through the use of an invariant measure as a prior measure in the Bayesian sense. In first-order OP, unresolved information is approximated by its conditional expectation with respect to the invariant measure. In higher-order OP, unresolved information is approximated by a stochastic estimator, leading to a system of random or stochastic differential equations. We explain the ideas through a simple example, and then apply them to the solution of Averaged Euler equations in two space dimensions