Neural Surrogate Model
A machine learning model trained to approximate the outputs of a physics-based simulation (FEA, CFD, thermal) for new input geometries and boundary conditions. Surrogates evaluate design candidates in milliseconds rather than hours, enabling large-scale design space exploration.
Why it matters
Design iteration bottlenecks at expensive simulation steps. Neural surrogates compress that bottleneck — enabling engineers to explore 100x more design candidates in the same wall-clock time, surfacing better solutions before production commitment.
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Cite this definition
Finocchiaro, Michael. “Neural Surrogate Model.” DemystifyingPLM PLM Glossary, 2026, https://www.demystifyingplm.com/glossary/neural-surrogate-model