TIDE is a physically diverse 3D turbulence benchmark dataset: 15 configurations of the same incompressible Navier-Stokes system along eight physics axes (forced isotropic, extended physics, free decay), each shipping 8-16 fully independent realizations at 256^3 in fp64 (134 trajectories, ~2.6 TB), released only after passing a fixed acceptance standard of statistical gates and equation-level residual checks. This record is the citable anchor for the release. It archives the datasheet, the deterministic benchmark-slice manifest, and a snapshot of the generation, acceptance, and benchmark code. The full corpus is hosted on HuggingFace; the code is developed at github.com/Dyloong1/TIDE-dataset-benchmark. Companion paper: TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning (under submission, ACM SIGKDD Datasets & Benchmarks Track).
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".