. In this paper we present a methodology aimed at systematically exploring the shape of the `envelope' of simulation trajectories and the implicit theory that a simulation represents. Thus it complements methods like Monte Carlo analysis, the inspection of single scenarios and syntactical

such simulations are used to represent social processes there are necessarily indeterministic and arbitrary aspects, which are typically represented as either random choices (or numbers) or constants chosen by the programmer. Each such `choice' means that the simulation takes one of the possible `trajectories

The implicit theory that a simulation represents is precisely not in the individual choices but rather in the `envelope' of possible trajectories -- what is important is the shape of the whole envelope. Typically a huge amount of computation is required when experimenting with factors

Abstract. This paper presents a method used for systematically investigating the content of a simulation model [see 17-18] in terms of constraint logic programming, more specifically as a forward chaining (model-based) constraint exploration of simulation trajectories. Possible applications

A numerical algorithm integrating the 3N Cartesian equations of motion of a system of N points subject to holonomic constraints is formulated. The relations of constraint remain perfectly fulfilled at each step of the trajectory despite the approximate character of numerical integration. The method

simulation methods to simulate trajectories of discrete, stochastic systems, (methods that are rigorously equivalent to the Master Equation approach) but these do not scale well to systems with many reaction pathways. This paper presents the Next Reaction Method, an exact algorithm to simulate coupled

The visualization of simulation trajectories is a well-established approach to analyze simulated processes. Likewise, the visualization of the parameter space that configures a simulation is a well-known method to get an overview of possible parameter combinations. This paper follows the premise

reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules. Due to the presence of integer variables, the resulting on-line optimization procedures are solved through Mixed Integer Quadratic Programming (MIQP

Abstract—As parallel algorithms and architectures drive the longest molecular dynamics (MD) simulations towards the millisecond scale, traditional sequential post-simulation data analysis methods are becoming increasingly untenable. Inspired by the programming interface of Google’s MapReduce, we

One of the challenges in the large-scale simulations required for many molecular systems (such as those of biological interested) is the recording, monitoring and visualization of configvrational information from molecular dynamics trajectories spanning millions, and sometimes billions, vf