To model the maximum temperature at three points of interest (POIs) during an inductive process, we faced a classic challenge: achieving high-fidelity accuracy (fine grid and temporal resolution) while minimizing computational effort.

Each simulation depends on three parameters across three segments—current, inductor y-offset, and velocity—leading to a complex, high-dimensional design space.

The Challenge

• A fully high-fidelity model would require a large number of expensive simulations.

• A purely low-fidelity model, while faster, leads to inaccurate and unreliable predictions.

• High-fidelity simulations are roughly 7× more computationally expensive than their low-fidelity counterparts.

• A direct comparison showed that using only low-fidelity data could lead to temperature prediction errors of up to 40%.

The Solution: Software X and Multifidelity Modeling

By using our multifidelity algorithm in Software X, we combined 20 high-fidelity and 69 low-fidelity simulations (89 total samples) to train a global model capable of accurately predicting high-fidelity temperature outcomes at all POIs.

Software X uses an adaptive Design of Experiments (DoE) approach:

• It begins with a small, correlated batch of low- and high-fidelity samples.

• A data-driven surrogate model is built.

• New sampling points are suggested iteratively, focusing on regions where high-fidelity insight is most valuable.

This strategy ensures that only the most essential high-fidelity simulations are performed, dramatically reducing overall compute time while maintaining prediction quality.

Results

• The multifidelity metamodel accurately reproduces the high-fidelity system behavior with an acceptable error rate.

• In contrast, a standard ML model trained on the same limited high-fidelity dataset performs significantly worse.

• This validates our approach: Exploiting low-fidelity to explore high-fidelity behavior efficiently and reliably.