Data-driven parameterization of the generalized Langevin equation

We present a data-driven approach to determine the memory kernel and random noise in generalized Langevin equations. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. We show that such an approximation can be constructed to arbitrarily high order and the resulting generalized Langevin dynamics can be embedded in an extended stochastic model without explicit memory. We demonstrate how to introduce the stochastic noise so that the second fluctuation-dissipation theorem is exactly satisfied. Results from several numerical tests are presented to demonstrate the effectiveness of the proposed method

The abundance of high-redshift objects as a probe of non-Gaussian initial conditions

The observed abundance of high-redshift galaxies and clusters contains precious information about the properties of the initial perturbations. We present a method to compute analytically the number density of objects as a function of mass and redshift for a range of physically motivated non-Gaussian models. In these models the non-Gaussianity can be dialed from zero and is assumed to be small. We compute the probability density function for the smoothed dark matter density field and we extend the Press and Schechter approach to mildly non-Gaussian density fields. The abundance of high-redshift objects can be directly related to the non-Gaussianity parameter and thus to the physical processes that generated deviations from the Gaussian behaviour. Even a skewness parameter of order 0.1 implies a dramatic change in the predicted abundance of z\gap 1 objects. Observations from NGST and X-ray satellites (XMM) can be used to accurately measure the amount of non-Gaussianity in the primordial density field.Comment: Minor changes to match the accepted ApJ version (ApJ, 539

A Process Algebra Software Engineering Environment

In previous work we described how the process algebra based language PSF can be used in software engineering, using the ToolBus, a coordination architecture also based on process algebra, as implementation model. In this article we summarize that work and describe the software development process more formally by presenting the tools we use in this process in a CASE setting, leading to the PSF-ToolBus software engineering environment. We generalize the refine step in this environment towards a process algebra based software engineering workbench of which several instances can be combined to form an environment

Software (Re-)Engineering with PSF II: from architecture to implementation

This paper presents ongoing research on the application of PSF in the field of software engineering and reengineering. We build a new implementation for the simulator of the PSF Toolkit starting from the specification in PSF of the architecture of a simple simulator and extend it with features to obtain the architecture of a full simulator. We apply refining and constraining techniques on the specification of the architecture to obtain a specification low enough to build an implementation from

Dimension reduction for systems with slow relaxation

We develop reduced, stochastic models for high dimensional, dissipative dynamical systems that relax very slowly to equilibrium and can encode long term memory. We present a variety of empirical and first principles approaches for model reduction, and build a mathematical framework for analyzing the reduced models. We introduce the notions of universal and asymptotic filters to characterize `optimal' model reductions for sloppy linear models. We illustrate our methods by applying them to the practically important problem of modeling evaporation in oil spills.Comment: 48 Pages, 13 figures. Paper dedicated to the memory of Leo Kadanof