Data-driven strategies for optimal bicycle network growth

Data-driven Optimal Transport

The problem of optimal transport between two distributions ρ(x) and μ(y) is extended to situations where the distributions are only known through a finite number of samples {xi} and {yj}. A weak formulation is proposed, based on the dual of Kantorovitch’s, with two main modifications: replacing the expected values in the objective function by their empirical means over the {xi} and {yj}, and re...

Data-driven optimal ILC for multivariable systems

Many iterative learning control algorithms rely on a model of the system. Although only approximate model knowledge is required, the model quality determines the convergence and performance properties of the learning control algorithm. The aim of this paper is to remove the need for a model for a class of multivariable ILC algorithms. The main idea is to replace the model by dedicated experimen...

Optimal data structures for stochastic driven simulations

Simulations where we have some prior information on the probability distribution of possible outcomes are common in many fields of science (physics, chemistry, biochemistry, etc). Optimal data structures that allow dynamic updates without regenerating the whole structure are crucial for both efficient and accurate results, especially at large scale. In this paper, we describe three different me...

Data-Driven Optimal Control for Adaptive Optics

Dynamic Decision and Data-Driven Strategies for the Optimal Management of Subsurface Geo-systems*

Effective geo-system management involves understanding of the interplay between surface entities (e.g., locations of injection and production wells in an oil reservoir) and appropriately effecting subsurface characteristics. This in turn requires efficient integration of complex numerical models of the environment, optimization procedures, and decision making processes. The dynamic, data-driven...