107 citations · 475 across the 51 of their papers we have counts for
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Data-driven initialization of deep learning solvers for Hamilton-Jacobi-Bellman PDEs
Anastasia Borovykh, Dante Kalise, Alexis Laignelet +1
A deep learning approach for the approximation of the Hamilton-Jacobi-Bellman partial differential equation (HJB PDE) associated to the Nonlinear Quadratic Regulator (NLQR) problem…
Supervised learning for kinetic consensus control
Giacomo Albi, Sara Bicego, Dante Kalise
In this paper, how to successfully and efficiently condition a target population of agents towards consensus is discussed. To overcome the curse of dimensionality, the mean field f…
Consensus based optimization via jump-diffusion stochastic differential equations
D. Kalise, A. Sharma, M. V. Tretyakov
We introduce a new consensus based optimization (CBO) method where interacting particle system is driven by jump-diffusion stochastic differential equations. We study well-posednes…
Data-driven Tensor Train Gradient Cross Approximation for Hamilton-Jacobi-Bellman Equations
Sergey Dolgov, Dante Kalise, Luca Saluzzi
A gradient-enhanced functional tensor train cross approximation method for the resolution of the Hamilton-Jacobi-Bellman (HJB) equations associated to optimal feedback control of n…
Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control
Sergey Dolgov, Dante Kalise, Luca Saluzzi
An optimized variant of the State Dependent Riccati Equations (SDREs) approach for nonlinear optimal feedback stabilization is presented. The proposed method is based on the constr…