Automotive & Mobility

Automotive Aerodynamics / Large-Scale CFD

MIT's large-scale automotive aerodynamics dataset: 8,000 parametric car geometries with CFD flow fields, meshes and aerodynamic coefficients.

Automotive Aerodynamics / Drag Prediction

Train surrogates on high-fidelity LES data for millisecond drag-coefficient (Cd) prediction, enabling digitalization and aero optimization of vehicle external flow.

ScientificML

Introduces a decision-epoch-based framework for dynamic electric autonomous dial-a-ride, jointly optimizing vehicle routing, scheduling, and charging decisions using combinatorial optimization and machine learning.

CFD

TM4FF introduces a physics-constrained operator learning framework combining Transformer attention with Mamba state-space models for accurate flow field prediction and denoising.

CFD/Fluid

A periodic physics-informed neural network (P-PINN) framework is proposed for solving parametric periodic flows and analyzing finite-amplitude aeroelastic responses in a purely physics-driven manner.