arXiv AI
AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD
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arXiv AI
Aug 19
ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flows
ChannelFlow-Tools is an open‑source, configuration‑driven pipeline that generates machine‑learning‑ready datasets for three‑dimensional obstructed channel flows. It combines procedural obstacle geometry generation across six shape families, signed‑distance‑field voxelisation, lattice‑Boltzmann simulation, and packaging into ML‑ready tensors, all driven by reproducible configuration files. The pipeline is validated through mesh‑integrity audits, SDF representation checks, solver benchmarks, and data‑integrity audits, and it has been used to train surrogate models (3D U‑Net, FNO, U‑FNO) that learn geometry‑to‑flow mappings and exhibit physically interpretable behaviour on out‑of‑distribution splits.
arXiv Machine Learning
Aug 17
Learning Unsteady Aneurysm Hemodynamics with Physics-Informed DeepONets
arXiv:2608. 13629v1 Announce Type: cross Abstract: Clinically actionable, patient-specific hemodynamic assessment, specifically wall shear stress, vortex structure and pressure distributions, is critical for determining risky or unfavorable evolution in Abdominal Aortic Aneurysms (AAA).
arXiv Machine Learning
Aug 17
Inpainting physics: self-supervised learning for context-driven fluid simulation
arXiv:2605. 08832v3 Announce Type: replace Abstract: Neural surrogate models for computational fluid dynamics (CFD) are typically trained as forward operators that map explicit problem specifications, such as geometry and boundary conditions, to solution fields.
arXiv Machine Learning
3d ago
HiLiftAeroML: A High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
HiLiftAeroML is the first open high‑fidelity CFD dataset focused on high‑lift aircraft aerodynamics, comprising 1,800 simulations across 180 variants of the NASA Common Research Model and ten angles of attack from 4° to 22°. Each case uses a GPU‑accelerated explicit wall‑modeled LES on grids of 300–500 million cells, covering attached, separated, and post‑stall flow conditions. The dataset, released under CC‑BY‑4.0, includes geometries, time‑averaged fields, integrated loads, validation data, and benchmark splits, and is accompanied by baseline models that perform well on interpolation and held‑out geometry tests but still struggle with high‑angle separated flow and out‑of‑distribution regimes.
arXiv AI
Sep 10
FSAN: Flow State Attention Network for Aerodynamic Prediction
arXiv:2609.06660v1 Announce Type: cross Abstract: Accurate aerodynamic prediction is critical for designing fuel-efficient and safe transportation systems such as aircraft and automobiles, yet tradit...
arXiv Machine Learning
Sep 4
Learnable composition for neural operators
The paper introduces LatentDDM, a neural operator framework that first pretrains on small subdomains and then adapts to new settings by training only a lightweight composition module. Experiments on steady Darcy flow and unsteady airfoil flow show that this approach reduces error by 36‑56% on larger domains and improves 20‑step rollouts, outperforming capacity‑matched full‑domain models. The study highlights co‑designed local pretraining and composition‑level transfer as a promising design principle for physical foundation models.