# Multifidelity Modeling

Bifidelity modeling uses cheap low-fidelity simulations to supplement scarce high-fidelity experiments, but the usual product is a single deterministic fit that says little about how trustworthy a prediction is. This work extends a single-fidelity Langevin sampling hypernetwork to bifidelity data, using a shared-latent data model that predicts both the low-fidelity response and the high-fidelity correction while a learned drift network generates parameter samples from the posterior score. On a synthetic hyperelastic case, conditional staged training cut the SPD-drift RMSE from 0.075 to 0.024 and the convex-potential drift from 0.081 to 0.017; on experimental and DNS data, predicting the high-fidelity response directly gave lower error than an additive discrepancy.

Bifidelity modeling uses cheap low-fidelity simulations to supplement scarce high-fidelity experiments, but the usual product is a single deterministic fit that says little about how trustworthy a prediction is. This work extends a single-fidelity Langevin sampling hypernetwork to bifidelity data, using a shared-latent data model that predicts both the low-fidelity response and the high-fidelity correction while a learned drift network generates parameter samples from the posterior score. On a synthetic hyperelastic case, conditional staged training cut the SPD-drift RMSE from 0.075 to 0.024 and the convex-potential drift from 0.081 to 0.017; on experimental and DNS data, predicting the high-fidelity response directly gave lower error than an additive discrepancy.