Zobrazeno 1 - 10
of 618
pro vyhledávání: '"Darbon, P."'
The interplay between stochastic processes and optimal control has been extensively explored in the literature. With the recent surge in the use of diffusion models, stochastic processes have increasingly been applied to sample generation. This paper
Externí odkaz:
http://arxiv.org/abs/2409.09614
We propose a novel neural network architecture (TSympOCNet) to address high--dimensional optimal control problems with linear and nonlinear dynamics. An important application of this method is to solve the path planning problem of multi-agent vehicle
Externí odkaz:
http://arxiv.org/abs/2408.03785
Uncertainty quantification (UQ) in scientific machine learning (SciML) combines the powerful predictive power of SciML with methods for quantifying the reliability of the learned models. However, two major challenges remain: limited interpretability
Externí odkaz:
http://arxiv.org/abs/2404.08809
Maximum entropy (Maxent) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, Maxent models need efficient optimization algorithms to sca
Externí odkaz:
http://arxiv.org/abs/2403.06816
We address two major challenges in scientific machine learning (SciML): interpretability and computational efficiency. We increase the interpretability of certain learning processes by establishing a new theoretical connection between optimization pr
Externí odkaz:
http://arxiv.org/abs/2311.07790
Hamilton-Jacobi partial differential equations (HJ PDEs) have deep connections with a wide range of fields, including optimal control, differential games, and imaging sciences. By considering the time variable to be a higher dimensional quantity, HJ
Externí odkaz:
http://arxiv.org/abs/2303.12928
Publikováno v:
Frontiers in Molecular Neuroscience, Vol 17 (2024)
Externí odkaz:
https://doaj.org/article/f4f17b666e4d4903ad607512cbccf292
Solving high-dimensional optimal control problems in real-time is an important but challenging problem, with applications to multi-agent path planning problems, which have drawn increased attention given the growing popularity of drones in recent yea
Externí odkaz:
http://arxiv.org/abs/2201.05475
Autor:
Darbon, Jérôme, Langlois, Gabriel P.
Logistic regression is a widely used statistical model to describe the relationship between a binary response variable and predictor variables in data sets. It is often used in machine learning to identify important predictor variables. This task, va
Externí odkaz:
http://arxiv.org/abs/2111.15426
Two key challenges in optimal control include efficiently solving high-dimensional problems and handling optimal control problems with state-dependent running costs. In this paper, we consider a class of optimal control problems whose running costs c
Externí odkaz:
http://arxiv.org/abs/2110.02541