Simplify surrogate model development with AI Physics Auto Tune, coming soon to Rescale. Set a search duration and let AutoTune explore hyperparameter configurations in parallel, remove weak candidates early, fully train the strongest options, and select the best model across four production-ready architectures, without requiring deep AI/ML expertise.
Develop aerodynamic AI Physics models with HiLiftAeroML, an open-source dataset now available natively within Rescale AI Physics. Built from surface-based simulation data across four geometries and multiple angles of attack, it gives engineers a practical starting point for training AI surrogate models without first assembling and building their own dataset.
Start training surrogate models faster with open-source AI Datasets now available in Rescale AI Physics. Engineers can now train models directly from the DrivAerML dataset, a subset of the high-fidelity open-source public dataset for automotive aerodynamics based on 500 parametrically morphed variants. Providing a great way to get started with AI Physics without having to first generate your own simulation dataset.
