Releases first open high-fidelity CFD dataset of 1800 samples from 180 variants of NASA high-lift CRM at 10 angles of attack using GPU-accelerated wall-modeled LES.
arXiv preprint arXiv:2411.17164 , year=
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Hamiltonian Graph Networks achieve 150-600x faster training via random feature parameter construction while retaining comparable accuracy and physical invariances on N-body systems up to 10,000 particles.
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HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
Releases first open high-fidelity CFD dataset of 1800 samples from 180 variants of NASA high-lift CRM at 10 angles of attack using GPU-accelerated wall-modeled LES.
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Rapid training of Hamiltonian graph networks using random features
Hamiltonian Graph Networks achieve 150-600x faster training via random feature parameter construction while retaining comparable accuracy and physical invariances on N-body systems up to 10,000 particles.