Parametric Optimization via Local AI Inference
Blog post from Rescale
Rescale previews upcoming local AI Physics inference capabilities that allow engineers to connect geometry, surrogate models, and outputs in lightweight workflows on their own workstations. The tooling is intended to support faster parametric studies and optimization loops without requiring dedicated inference servers or HPC clusters, enabling users to explore more design options locally. The announcement includes a demonstration of the planned capabilities and positions them as part of Rescale’s broader AI Physics offering.
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