Ashmita Bhattacharya

?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. Abstract The practical use of multi-agent reinforcement learning (MARL) solutions for real-world bridge networks comprising thousands of structures and subject to multiple uncertainties and constraints remains an open optimization challenge.

Abstract Climate change-related risk mitigation is typically addressed using cost-benefit analysis that evaluates mitigation strategies against a wide range of simulated scenarios and identifies a static policy to be implemented, without considering future observations. Due to the substantial uncertainties inherent in climate projections, this identified policy will likely be sub-optimal with respect to the actual climate trajectory that evolves in time.

In recent years, there has been an increasing interest in the use of graph neural networks (GNNs) for analyzing dynamic graphs, which are graphs that evolve over time. However, there is still a lack of understanding of how different temporal graph neural network (TGNs) configurations can impact the accuracy of predictions on dynamic graphs.