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Found 6 papers, 0 papers with code
Data/moment-driven approaches for fast predictive control of collective dynamics
The high-dimensional character of collective dynamics hampers the performance of traditional MPC algorithms based on fast online dynamic optimization at every time step.
Mean field limits for discrete-time dynamical systems via kernel mean embeddings
Mean field limits are an important tool in the context of large-scale dynamical systems, in particular, when studying multiagent and interacting particle systems.
On kernel-based statistical learning in the mean field limit
In many applications of machine learning, a large number of variables are considered.
Reproducing kernel Hilbert spaces in the mean field limit
Kernel methods, being supported by a well-developed theory and coming with efficient algorithms, are among the most popular and successful machine learning techniques.
Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn
Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest.
Stability analysis of a hyperbolic stochastic Galerkin formulation for the Aw-Rascle-Zhang model with relaxation
We investigate the propagation of uncertainties in the Aw-Rascle-Zhang model, which belongs to a class of second order traffic flow models described by a system of nonlinear hyperbolic equations.
Numerical Analysis Numerical Analysis 35L65, 90B20, 35R60
