Harnessing the power of deep surrogate models
Barbara Hammer
Research Institute for Cognitive Interaction Technology (CITEC), Bielefeld University
Abstract
Recent advances in deep learning carry the promise to substitute computationally costly simulations or partially unobservable dynamics by deep surrogate models which are trained on example data. Since the resulting deep model is typically fast to evaluate, it is given as an explicit analytic function, and it generalizes beyond the observed training signals, deep surrogates carry diverse promises depending on the specific scenario and downstream task: They allow for a fast approximation of complex dynamic behavior; they enable real-world state inference given partial information; they support efficient system optimization based on gradient information. On the downside, deep surrogates face possibly time-consuming training of deep models; further, the provision of training data might be computationally complex, possibly infeasible, as it is typically based simulations itself.
In the talk, I will focus on opportunities and challenges of surrogate models for a technical application, namely hydraulic simulation of water distribution systems. I will demonstrate the power of graph neural networks to learn simulations of the dynamics such that inference based on limited information becomes possible. I will have a look at how to avoid the computational challenge of huge simulations to provide training data by means of physics-informed methods, which substitute training observation by laws of physics. Further, I will have a glimpse at specific downstream tasks, which become possible based on the surrogate.
Speaker
Dr. Barbara Hammer chairs the Machine Learning research group at the Research Institute for Cognitive Interaction Technology (CITEC) at Bielefeld University. After completing her doctorate at the University of Osnabrück in 1999, she was Professor of Theoretical Computer Science at Clausthal University of Technology and a visiting researcher in Bangalore, Paris, Padua and Pisa. Her areas of specialisation include trustworthy AI, lifelong machine learning, and the combination of symbolic and sub-symbolic representations. She is PI in the ERC Synergy Grant WaterFutures and in the DFG Transregio Contructing Explainability. Barbara Hammer has been active at IEEE CIS as member of chair of the Data Mining Technical committee and the Neural Networks Technical Committee. She has been elected as a review board member for Machine Learning of the German Research Foundation in 2024 and she represents computer science as a member of the selection committee for fellowships of the Alexander von Humboldt Foundation. She is member of the Scientific Directorate Schloss Dagstuhl. Further, she has been selected as member of Academia Europaea.