Sim+Real: Joint Simulation-Experiment Training Improves Balanced Prediction in Physical Systems
The study investigates joint training for simulation and experiment prediction by formulating it as a multi-objective learning problem. Joint training balances simulation risk and experimental risk, leading to superior performance across various evaluation settings compared to sequential fine-tuning. It shows that combining both domains preserves simulation knowledge while adapting to experimental data, resulting in the best balanced prediction accuracy.