Adding BSC-Inria project

collections/_projects/hpc_and_ai_for_electromagnetics.md

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+---
+layout: post
+title: Extreme-scale AI and computing for high-fidelity electromagnetics
+date: 2026-05-26
+updated: 2026-05-26
+navbar: Research
+subnavbar: Projects
+project_url:
+status: running
+topics:
+  - numerics
+  - ai
+  - apps 
+keywords: computational electromagnetics (CEM), reduced order models (ROM), deep neural network (DNN)
+head:
+  - delapuente_j
+  - lanteri_s 
+members:
+  - agullo_e
+  - castillo_o
+  - circuns_m
+  - farnos_j
+  - guillet_c
+  - giraud_l
+  - lopez_h
+  - marait_g
+  - modesto_d
+  - sylvand_g
+---
+
+## Research topic and goals
+
+Electromagnetic (EM)  waves are  ubiquitous in our  daily environment.
+They find applications of social  and environmental relevance, as well
+as in technology for industry and defense. The scales resolved involve
+the micrometer scale in optoelectronics to the hundreds of km involved
+in radar detection.  Computational  electromagnetics refers to the use
+of  numerical modelling  for the  study  of EM  wave interaction  with
+objects and matter.  The participant groups from BSC and  Inria have a
+long-standing  expertise  on  the development  and  implementation  of
+computational   electromagnetic  methods   including  conforming   and
+high-order finite element methods (FEM),  method of moments – boundary
+element methods (MoM/BEM) and  discontinuous Galerkin (DG).  These are
+considered as  high-fidelity methods and the  corresponding so- called
+fullwave  solvers are  materialized  in  in-house simulation  software
+based on  numerical algorithms that  have been extensively  adapted to
+high  performance computing  hardware possibly  combing multicore  CPU
+chips and GPU accelerators. From the point of view of applications, an
+even more important objective  of computational electromagnetics is to
+shape the  EM wave interactions, or  to solve the inverse  problem for
+unveiling the values of physical  parameters of the underlying problem
+whose  mathematical   modeling  relies   on  the  system   of  Maxwell
+equations. These  contexts are  treated with an  outer loop  driving a
+numerical   optimization   workflow,   meaning  that   many   fullwave
+simulations need to be  performed therefore drastically increasing the
+computational load and ultimately  preventing the study of large-scale
+problems.
+
+Our  overarching  objective  is  to design,  develop  and  demonstrate
+disruptive numerical approaches  leveraging high performance computing
+and  AI-driven methodologies  for addressing  the current  performance
+bottlenecks that  are faced when  running inverse design  or parameter
+inversion  studies  for large-scale  EM  wave  interactions. From  the
+methodological point  of view,  we have  identified three  main topics
+that  will underly  specific  objectives of  our  joint research:  (1)
+Numerical  Linear  Algebra (NLA);  (2)  HPC  and AI;  (3)  Data-driven
+reduced-order modeling (ROM).
+
+## Visits and meetings
+
+## Impact and publications
+
+### Papers
+
+### Funding
+
+### Impact on other projects
+
+## Future plans
+
+## References