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Found 7 papers, 3 papers with code
Neural parameter calibration and uncertainty quantification for epidemic forecasting
The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus.
Machine Learning for the identification of phase-transitions in interacting agent-based systems
Deriving closed-form, analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs).
Inferring networks from time series: a neural approach
Network structures underlie the dynamics of many complex phenomena, from gene regulation and foodwebs to power grids and social media.
Neural parameter calibration for large-scale multi-agent models
Computational models have become a powerful tool in the quantitative sciences to understand the behaviour of complex systems that evolve in time.
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents
In this chapter, we identify fundamental geometric structures that underlie the problems of sampling, optimisation, inference and adaptive decision-making.
On stochastic mirror descent with interacting particles: convergence properties and variance reduction
A second alternative is to use a fixed step-size and run independent replicas of the algorithm and average these.
The sharp, the flat and the shallow: Can weakly interacting agents learn to escape bad minima?
As a first step towards understanding this question we formalize it as an optimization problem with weakly interacting agents.
