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SCROLLER - A Stochastic ContROL approach to machine Learning with applications to Environmental Risk models

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About the project

The main idea of ​​the SCROLLER project is to study the connections between stochastic analysis, risk theory and machine learning.

Stochastic analysis is the mathematical study of uncertainty over time. In particular, stochastic optimal control theory is a tool for making optimal decisions over time under uncertainty. The reason for working with stochastic models, as opposed to deterministic ones, is that most real-life problems are influenced by uncertain factors. Weather, politics, climate change and human actions are potential sources of uncertainty.

During the last decade, there has been a vast technological development and growth in computational power. In addition, digitization implies that
big data is available in many different settings. Machine learning is a set of mathematical algorithms and techniques which enable computers to improve at performing tasks with experience. Examples of ML algorithms are neural networks and reinforcement learning.

Machine learning algorithms can lead to wrong conclusions if we are
not careful in understanding the underlying mathematics. Though the experimental results of machine learning are good, there is still a lack of understanding of the mathematical reasons for these results. In particular, the literature concerning the connections between machine learning and stochastic analysis is sparse. The main purpose of the SCROLLER project is to study these connections.

In choice of applications throughout the SCROLLER project, we will focus on problems related to environmental and climate risks. For instance, we
will work on degradation models with respect to environmental risk factors. We will use environmental contours for safer risk assessment of structures exposed to extreme environmental events. Due to climate change, there is more extreme weather, and in general more uncertainty regarding the future. We hope that this project can contribute to derive suitable risk assessments which take this change into account.


This project is funded by the  Reseach Council of Norway . Funding ID: 299897


Tags: Stochastics. Stochastic optimal control. Machine learning. Environmental applications.
Published Sep. 4, 2020 10:49 AM - Last modified June 10, 2021 3:17 PM