Research fellow, PhD Candidate (University of Kassel)
Energy system transiton modelling * stakeholder and market modelling, governance and policy modelling, * agent-based modelling (ABM), optimisation, * model coupling, open and integrative modelling framework, * open source, S4F
Interested in numerical models and new conceptual ideas, applications from industry to medicine.
I focus on numerical modeling of mechanics of solid materials and cell mechanics. The models that I developed so far address granular matters, bio-fluids, cellular tissues, and individual cells.
I further develop Agent-based Models, which are methods to predict collective behavior from individual dynamics controlled by rules or differential equations. Examples: tumor growth, swarms, crowd movement.
The methods I used are Particle-based methods which offer great flexibility within physical modeling, and can operate in a large range of scales, from atomistic scales (e.g. Molecular Dynamics) to continuum approaches (e.g. Smoothed Particle Hydrodynamics).
Agent Based Modelling
I develop simulation tools for generating what-if scenarios for decision making. I predominantly use Agent-Based Modelling (ABM) technique as most of my simulations model complex systems. In some cases, I have extended existing tools with modifications to model the given system. Although the tools are meant for research purposes, I have followed industry friendly delivery mechanisms, such as unit-tests, automated builds and delivery on cloud platforms.
Volker Grimm currently works at the Department of Ecological Modelling, Helmholtz-Zentrum für Umweltforschung. Volker does research in ecology and biodiversity research.
How to model it: Ecological models, in particular simulation models, often seem to be formulated ad hoc and only poorly analysed. I am therefore interested in strategies and methods for making ecological modelling more coherent and efficient. The ultimate aim is to develop preditive models that provide mechanstic understanding of ecological systems and that are transparent and structurally realistic enough to support environmental decision making.
Pattern-oriented modelling: This is a general strategy of using multiple patterns observed in real systems as multiple criteria for chosing model structure, selecting among alternative submodels, and inversely determining entire sets of unknown model parameters.
Individual-based and agent-based modelling: For many, if not most, ecological questions individual-level aspects can be decisive for explaining system-level behavior. IBM/ABMs allow to represent individual heterogeneity, local interactions, and/or adaptive behaviour
Ecological theory and concepts: I am particularly interested in exploring stability properties like resilience and persistence.
Modelling for ecological applications: Pattern-oriented modelling allows to develop structurally realistic models, which can be used to support decision making and the management of biodiversity and natural resources. Currently, I am involved in the EU project CREAM, where a suite of population models is developed for pesticide risk assessment.
Standards for model communication and formulation: In 2006, we published a general protocol for describing individual- and agent-based models, called the ODD protocol (Overview, Design concepts, details). ODD turned out to be more useful (and needed) than we expected.
My research interests include policy informatics and decision making, modeling in policy analysis and management decisions, public health management and policy, and the role of public value in policy development. I am particularly interested in less mainstream approaches to modeling that account for learning, feedback, and other systems dynamics. I include Bayesian inference, agent-based models, and behavioral assumptions in both my research and teaching.
In my dissertation research, I conceptualize state Medicaid programs as complex adaptive systems characterized by diverse actors, behaviors, relationships, and objectives. These systems reproduce themselves through both strategic and emergent mechanisms of program management. I focus on the mechanism by which citizens are sorted into or out of the system: program enrollment. Using Bayesian regression and agent-based models, I explore the role of administrative practices (such as presumptive eligibility and longer continuous eligibility periods) in increasing enrollment of eligible citizens into Medicaid programs.
coupling of agent-based models and mathematical models
machine learning algorithms
deep learning algorithms
infectious diseases modeling