A simpler characterization of GBD
18 Sep 2025, 10:00 — Room 704, UniGe DIBRIS/DIMA, Via Dodecaneso 35
![Antonin Chambolle - [Mento Guancia Sopracciglio]](/media/images/Antonin_Chambolle_o8ioc6_PBDSJ8e.width-800.jpg)
Speaker:
Antonin Chambolle — CNRS senior scientist at CEREMADE, Université Paris-Dauphine, Paris
Antonin Chambolle — CNRS senior scientist at CEREMADE, Université Paris-Dauphine, Paris
Abstract:
The space GBD, introduced by G. Dal Maso to address variational problems in fracture mechanics, is characterized by slicing properties of the displacements, and a control of the 1D slices in all directions, which is not natural in this context. We give a simple proof that controlling d(d + 1)/2 directions in dimension d is sufficient to characterize properly GBD displacements. This is joint with Vito Crismale (Roma La Sapienza).
The space GBD, introduced by G. Dal Maso to address variational problems in fracture mechanics, is characterized by slicing properties of the displacements, and a control of the 1D slices in all directions, which is not natural in this context. We give a simple proof that controlling d(d + 1)/2 directions in dimension d is sufficient to characterize properly GBD displacements. This is joint with Vito Crismale (Roma La Sapienza).
Bio:
Antonin Chambolle is a CNRS senior scientist at CEREMADE, Université Paris-Dauphine, Paris, France since 2020. Prior, he was working at CMAP, Ecole Polytechnique. He has been working on the mathematical and numerical analysis of variational problems which arise in image analysis, computer science, materials science. He has worked as well in mathematical optimization, with an interest for first order methods for (convex) problems which arise in image processing and machine learning.
Antonin Chambolle is a CNRS senior scientist at CEREMADE, Université Paris-Dauphine, Paris, France since 2020. Prior, he was working at CMAP, Ecole Polytechnique. He has been working on the mathematical and numerical analysis of variational problems which arise in image analysis, computer science, materials science. He has worked as well in mathematical optimization, with an interest for first order methods for (convex) problems which arise in image processing and machine learning.