Probabilistic machine learning and phenomenological knowledge. Developments for optimization under uncertainty in safety-critical systems

Abstract

Artificial Intelligence (AI) and data-driven decisions based on Machine Learning (ML) are making an impact on an increasing number of industries. As these autonomous and self-learning systems become more and more responsible for decisions that may ultimately affect the safety of people, assets, or the environment, ensuring the safe use of AI will be crucial.

This thesis aims to provide some of the tools needed to make data-driven modeling suitable for use in safety-critical systems, like a ship, offshore structure, or a spacecraft. This is challenging when we are faced with complex physical phenomena, in environments with a high degree of uncertainty, and where the consequence of an erroneous decision can be catastrophic. To succeed, the knowledge we possess about these phenomena must be exploited optimally.

We consider various ways in which knowledge about the underlying physical system can be incorporated into probabilistic models. This includes how to make use of expensive computer simulations most efficiently, and how physics-based knowledge can be used as constraints to obtain “physically obedient machine learning models”. With this approach, we develop algorithms that can be used to search for optimal decisions in uncertain and safety-critical environments.