Modeling across Multiple Scales and Disciplines

Our research is devoted to developing robust, reliable, and most of all accurate models that allow to predict desired quantities, e.g., a materials elastic, optical, or electronic properties from its molecular constituents; forecasting the weather, and, on geological time scales, the tectonic movement of plates. 

Description, prediction and understanding of natural phenomena is at the core of all fields of physical and life sciences. The empirical data they provide, combined with tools from mathematics and logic, are fed into numerical models in order to explain observations in a quantitative and predictive manner. A particular challenge is to make connections and predictions of properties and processes across different scales. 

Even though the details and processes at the different scales vary significantly, the modeling approaches to tackle them are rather similar.

Research Avenue A:
Novel Algorithms

Research Avenue B:

Rain clouds

Research Avenue C:
Data-Driven Modeling

Data driven matrix

Ongoing Research Projects

M3ODEL provides funding for PhD interdisciplinary projects that advance computational models to tackle problems such as protein folding, chiral structures, groundwater flow, seismic wave fields, and atmospheric flow among others.

M3ODEL also supports a research project by Dr. Mattia Mazzucchelli, a Humboldt Fellow from June 2021, who is collaborating with Profs. Moulas and Speck to combine molecular dynamics with continuum mechanics simulations to understand how phase equilibria are affected by the non-hydrostatic stress that develop in the Earth during mineral and rock deformation.