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PIM

Propagation and Multi-scale Interaction

The work carried out within the PIM team aims to develop and improve modeling and optimization tools to help in the representation and understanding of phenomena resulting from the interaction of electromagnetic waves with the environment. These phenomena are observed at different scales of the system, but also devices and components developed by other teams DH and SMART.

Research is carried out in the framework under three complementary  topics

  • MOSEM: Modeling & Simulation EM - near & far field (tools, asymptotic methods, exact methods, empirical methods, hybrids methods, ...)
  • MOCAP: Modeling and characterization of the propagation channel: physical and statistical methods, ...
  • MOSSYP: Modeling and simulation systems and platforms (experimental & Virtual Systems)

The team has several experimental facilities including devices and tools such as :

  • Anechoic chamber works from 2 to 18 GHz
  • Operational Radar operating in the X band
  • Experimental Radar operating in the Ku band
  • Several simulation platforms (high performance servers)
  • Specialized software: HFFS, FEKO, ADS, CST-MWS, MicroStripes, Winprop, IE3D, SolidWorks, OrCAD ...

You will find below a non-exhaustive list of the various partners with which PIM maintains solid collaborations.

 

Map partenaires

News
Latest announcements

Advances in Radar Remote Sensing Processing and Applications

PIM  

We are pleased to announce the opus of the Special Issue is now coming: "Artificial Intelligence-Based Target Recognition and Remote Sensing Data Processing"   https://www.mdpi.com/journal/sensors/special_issues/00FZ07WE66 Deadline: 20 March 2025   #…

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Young Scientist Award (URSI AT-RASC 2024)

PIM  

Thomas Bonnafont received a Young Scientist award from the paper “Modeling of a biological cell exposed to an electrical pulse: a Discrete Dual Finite Volume method application” during the 4th URSI Atlantic Radio Science Meeting (AT-RASC 2024), Gran …

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Best Paper Award (SPI 2024)

PIM  

The paper "Modeling of IC Buffers from Channel Responses via Machine Learning Kernel Regression" from R. Trinchehero, T. Bardde, M. Telescu and I. S. Stievano received a Best Paper Award during the 28th IEEE Workshop on Signal an Power Integrity (SPI…

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