ML assisted RF filter design

Simulation of a porous medium subjected to high speed impacts

The control of the dynamic response of complex materials (foam, ceramic, metal, composite) subjected to intense solicitations (energy deposition, hypervelocity impact) is a major issue for many applications developed and carried out French Atomic Energy Commission (CEA). In this context, CEA CESTA is developing mathematical models to depict the behavior of materials subjected to hypervelocity impacts. Thus, in the context of the ANR ASTRID SNIP (Numerical Simulation of Impacts in Porous Media) in collaboration with the IUSTI (Aix-Marseille Université), studies on the theme of modeling porous materials are conducted. They aim to develop innovative models that are more robust and overcome the theoretical deficits of existing methods (thermodynamic consistency, preservation of the entropy principle). In the context of this post-doc, the candidate will first do a literature review to understand the methods and models developed within IUSTI and CEA CESTA to understand their differences. Secondly, he will study the compatibility between the model developed at IUSTI and the numerical resolution methods used in the hydrodynamics computing code of the CEA CESTA. He will propose adaptations and improvements of this model to take into account all the physical phenomena that we want to capture (plasticity, shear stresses, presence of fluid inclusions, damage) and make its integration into the computation code possible. After a development phase, the validation of all this work will be carried out via comparisons with other existing models, as well as the confrontation with experimental results of impacts from the literature and from CEA database.

Computational statistics for post-flight analysis in atmospheric reentry

The post-doctorate corresponds to the context of flight tests of an instrumented vehicle (space shuttle, capsule or probe) which enters into the atmosphere. The aim is to reconstruct, from measurements (inertial unit, radar, meteorological balloon, etc.), the trajectory and various quantities of interest, in order to better understand the physical phenomena and to validate the predictive models. We focus on Bayesian statistics, associated with Markov chain Monte Carlo (MCMC) methods. The post-doctoral fellow will develop and extend the proposed approach and will benefit from a scientific collaboration with Audrey Giremus, professor at the University of Bordeaux and specialist in the field. We will in particular try to increase the performance of high dimensional sampling. Special attention will be paid to the machine learning issue of the exploitation of an aerological database. The final objective will consist in developping an evolving software prototype dedicated to the post-flight analysis of flight tests, that exploits the various sources of information. The evaluations will be based on simulated and real data, with comparison to existing tools. The collaboration work will lead to scientific communications and publications.

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