We aim at developing a theory allowing the inverse design in the absence of backward uniqueness to be addressed both for linear and nonlinear problems. We shall take advantage of the fact that initial data recovered by backward weak but not entropic solutions can lead to the desired target by the forward entropic flow. This leads to the interesting and non-standard question of building numerical schemes to approximate non-entropic weak solutions. This program will also be developed in the context of Hamilton-Jacobi equations where, among the wide class of weak solutions, the physical ones are characterized by the viscosity criterion.
The aim of this work is to recover the initial sparse sources that lead to a given final measurements using the diffusion equation. It is assumed that the initial condition can be written down as a linear combination of unitary deltas and their weights. In that context, an algorithm that combines the adjoint methodology with least squares is presented. In particular, the adjoint methodology is used to find the localization of the sparse sources and least squares to find the corresponding intensities.
Autonomous Vehicles (briefly AVs) have already been tested on urban and highway networks and, for the future they may be used to monitor and manage the vehicular traffic. The impact of autonomous vehicles on vehicular traffic is modeled by a PDE+ODE models with moving point-flux constraint. The PDE is a nonlinear hyperbolic conservation laws and the ODE represents the trajectory of autonomous vehicles. We show that the data collected by autonomous vehicles' sensors can be used to reconstruct the traffic state.
Various discrete adjoint methodologies are discussed for the inverse design of linear transport equations in 2 space dimensions
Tracking control of 1D scalar conservation laws in the presence of shocks
Numerical approximation of the inverse design problem for the Burgers equation