Quarter Truck SciML Demo

Download this exampleQuarterTruckSciML.zipFull project — Dyad model + Julia + data

An end-to-end SciML demonstration built on a quarter-truck ride-comfort model. It combines neural-network gray-box discovery — recovering tire cubic stiffness, Coulomb friction, and viscoelastic seat damping from sine-excited training data — with parameter calibration that recovers body mass and suspension stiffness, damping, and friction from ISO 8608 road measurements.

Note

This is a heavy SciML demo. The model diagram below renders from a snapshot, but the training and calibration runs are not executed in the documentation build. Run them from the QuarterTruckSciML project.

The model

QuarterTruckFullNN assembles the tire, body, seat, and driver masses with the suspension elements, an ISO 8608 road source, and a neural-network learning block:

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Quarter Truck SciML Demo ​

The model ​

Quarter Truck SciML Demo

The model