Sim+Real: Joint Simulation - Experiment Training Improves Balanced Prediction in Physical Systems

The paper introduces a joint simulation–experiment training framework that treats simulation and experimental data as separate objectives in a multi‑objective learning problem. Experiments on four fluid systems show that joint training outperforms both simulation‑only and experiment‑only baselines, as well as the conventional simulation‑to‑experiment fine‑tuning approach, by achieving a more balanced performance across domains and better retaining simulation‑specific information. The authors demonstrate that joint training preserves simulation‑only fields that are absent from experimental measurements, leading to improved overall predictive accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• fine-tuning