Romain Petit's Avatar

Romain Petit

@romainpetit

Postdoc at ENS Paris https://rpetit.github.io

40
Followers
62
Following
8
Posts
23.11.2024
Joined
Posts Following

Latest posts by Romain Petit @romainpetit

Preview
A convex lifting approach for the Calderón problem The Calderón problem consists in recovering an unknown coefficient of a partial differential equation from boundary measurements of its solution. These measurements give rise to a highly nonlinear for...

Alternatively, we proposed in a recent preprint with Giovanni S. Alberti and Simone Sanna a new reconstruction approach based on lifting and convex relaxation. It seems amenable to regularization, but its computational viability still needs to be investigated.

arxiv.org/abs/2507.00645

25.07.2025 13:54 👍 0 🔁 0 💬 0 📌 0

Take-home message: the optimization landscape of the least squares objective warrants further investigations. An interesting and challenging future direction is to study this landscape in the noisy regularized setting.

25.07.2025 13:54 👍 0 🔁 0 💬 1 📌 0
Preview
GitHub - rpetit/RadialCalderon.jl: Numerical resolution of the Calderon problem with piecewise constant radial conductivities Numerical resolution of the Calderon problem with piecewise constant radial conductivities - rpetit/RadialCalderon.jl

Finally, we provide an open source Julia package called RadialCalderon.jl, which allows to reproduce all our experiments. It might also serve as a benchmark for further studies on this piecewise constant radial version of the Calderón problem.

github.com/rpetit/Radia...

25.07.2025 13:54 👍 0 🔁 0 💬 1 📌 0

We also investigate the performance of a recently proposed approach based on convexification. For the first time, we propose a way to implement the former and show that it is consistently outperformed by Newton-type least squares solvers, which are also faster and require less measurements.

25.07.2025 13:54 👍 0 🔁 0 💬 1 📌 0
Post image

We revisit this issue in the case of radial piecewise constant conductivities, and prove that there are no local minimums in the case of two scalar unknowns with no measurement noise. We provide a partial proof in the general setting under a numerically verifiable assumption.

25.07.2025 13:54 👍 0 🔁 0 💬 1 📌 0

This inverse problem is highly ill-posed. The associated forward map (the mapping from the unknown to the measurements) is also nonlinear. For this reason, the classical least squares approach is generally believed to lead to a nonconvex optimization problem which is riddled with bad local minimums.

25.07.2025 13:54 👍 0 🔁 0 💬 1 📌 0
Post image

In electrical impedance tomography, one wishes to recover the conductivity of a medium from boundary current-voltage measurements. Calderón formalized this as an inverse problem for a PDE, whose goal is to estimate a coefficient of the PDE from measurements of its solution.

25.07.2025 13:54 👍 0 🔁 0 💬 1 📌 0
Post image

New preprint: "On the non-convexity issue in the radial Calderón problem" with Giovanni S. Alberti and Clarice Poon. We revisit the non-convexity issue for the least squares approach to the Calderón problem in the case of piecewise constant radial conductivities.

arxiv.org/abs/2507.03379

25.07.2025 13:54 👍 1 🔁 0 💬 1 📌 0