3 papers
physics.flu-dyn2025
Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations
Giovanni Catalani, Jean Fesquet, Xavier Bertrand +3
This paper introduces a novel surrogate modeling framework for aerodynamic applications based on Neural Fields. The proposed approach, MARIO (Modulated Aerodynamic Resolution Invar…
cs.LG2025
Geometry aware inference of steady state PDEs using Equivariant Neural Fields representations
Giovanni Catalani, Michael Bauerheim, Frédéric Tost +2
Advances in neural operators have introduced discretization invariant surrogate models for PDEs on general geometries, yet many approaches struggle to encode local geometric struct…
cs.CE2024
Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations
Giovanni Catalani, Siddhant Agarwal, Xavier Bertrand +3
This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The propo…