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Why study different natural systems?

Natural disasters push the process of scientific discovery to its limits: The enormous scale of extreme events makes them difficult to recreate in the laboratory, their destructive power and rare occurrence limits our ability to acquire field data, and the multitude of potentially relevant processes poses significant modeling challenges. But extremes in different natural systems share a common theme: The dynamic interactions between multiple solid and fluid phases can give rise to drastic nonlinearities that govern abrupt change. While the possibilities for instability are nearly endless in multiphase physics, Earth is a natural laboratory for understanding which of these instability matter in the sense that they can trigger rapid and extreme change in system behavior. 

As Einstein said: "Look deep into nature and you will understand everything better." We adopt this point of view in our research: We learn about natural disasters by modeling the multi-scale, multi-phase processes that trigger instability. And we learn about multi-phase physics by formulating models that can be tested against observational data from multiple different scales in different natural systems. 

In practice, most of our research focuses on developing mathematical models, customized for the problem at hand and validated against observational data from a broad spectrum of scales. We focus primarily on process-based models that can shed new light on the fundamental physics of natural systems, leveraging a large tool box of numerical and analytical techniques. Our research is hence at the interface between Earth science and applied mathematics.

An important motivation for research in natural hazards is to reduce the threat they pose to communities at risk. In some cases, risk mitigation is limited by an incomplete understanding of the underlying physical processes. In others, understanding the underlying physics might not be possible yet or may not be enough. To reduce risk when studying hazards is not enough, we work with decision-makers and communities to co-produce actionable knowledge and make our growing scientific understanding actionable.