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Found 5 papers, 2 papers with code
ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)
The aim of this competition is to encourage the development of new ML techniques to solve physical problems using a unified evaluation framework proposed recently, called Learning Industrial Physical Simulations (LIPS).
INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equations
For numerical design, the development of efficient and accurate surrogate models is paramount.
Multi-Objective Hull Form Optimization with CAD Engine-based Deep Learning Physics for 3D Flow Prediction
In this work, we propose a built-in Deep Learning Physics Optimization (DLPO) framework to set up a shape optimization study of the Duisburg Test Case (DTC) container vessel.
An extensible Benchmarking Graph-Mesh dataset for studying Steady-State Incompressible Navier-Stokes Equations
Recent progress in \emph{Geometric Deep Learning} (GDL) has shown its potential to provide powerful data-driven models.
Multi-scale Physical Representations for Approximating PDE Solutions with Graph Neural Operators
In this work, we study three multi-resolution schema with integral kernel operators that can be approximated with \emph{Message Passing Graph Neural Networks} (MPGNNs).
