Simulation Metamodeling

Simulation metamodeling aims to create fast, accurate surrogate models that approximate computationally expensive simulations, enabling efficient exploration of design spaces and real-time decision-making. Current research emphasizes developing robust and interpretable metamodels using diverse techniques, including physics-informed machine learning, generative models (like variational autoencoders and quantile regression-based methods), and advanced active learning strategies coupled with explainable AI methods like SHAP values. This field is crucial for accelerating scientific discovery and engineering design across various domains, from structural engineering and air traffic management to materials science and robotics, by significantly reducing the computational burden of complex simulations.