From Models to Simulations

This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations

An Investigation into Animating Plant Structures within Real-time Constraints

This paper is an analysis of current developments in rendering botanical structures for scientic and entertainment purposes with a focus on visualising growth. The choices of practical investigations produce a novel approach for parallel parsing of difficult bracketed L-Systems, based upon the work of Lipp, Wonka and Wimmer (2010). Alongside this is a general overview of the issues involved when looking at growing systems, technical details involving programming for the Graphics Processing Unit (GPU) and other possible solutions for further work that also could achieve the project's goals

A Markovian framework to formalize stochastic L-systems and application to models of plant development

This document is an extension of the article written by \textit{Loi and Cournède} (DMTCS, 2008). This article shows the relationship between stochastic L-Systems and a simplified GreenLab growth model with only branching and differentiation. By writing the probability generating function corresponding to each phenomenon and by compounding them, we get the expected values of the numbers of metamers of each type in the whole plant. In this report, we recall the main results of this article. In addition, we show how to derive the generating function in the general case when growth units contain a random number of metamers. We also get a recursive equation to compute the variance of the numbers of metamers of each type in the plant. Finally, we illustrate the results throughout Monte-Carlo simulations in four cases

Generating Functions of Stochastic L-Systems and Application to Models of Plant Development

International audienceIf the interest of stochastic L-systems for plant growth simulation and visualization is broadly acknowledged, their full mathematical potential has not been taken advantage of. In this article, we show how to link stochastic L-systems to multitype branching processes, in order to characterize the probability distributions and moments of the numbers of organs in plant structure. Plant architectural development can be seen as the combination of two subprocesses driving the bud population dynamics, branching and differentiation. By writing the stochastic L-system associated to each subprocess, we get the generating function associated to the whole system by compounding the associated generating functions. The modelling of stochastic branching is classical, but to model differentiation, we introduce a new framework based on multivariate phase-type random vectors

40 Years Theory and Model at Wageningen UR

"Theorie en model" zo luidde de titel van de inaugurele rede van CT de Wit (1968). Reden genoeg voor een (theoretische) terugblik op zijn wer