Uncertainty Aware Surrogate Model

Uncertainty-aware surrogate models aim to create accurate and reliable approximations of complex, computationally expensive simulations while explicitly quantifying the uncertainty inherent in these approximations. Current research focuses on improving the accuracy and efficiency of these models using various techniques, including Bayesian neural networks, denoising diffusion probabilistic models, and ensemble methods like conditional generative adversarial networks (cGANs). This field is crucial for advancing applications across diverse domains, enabling more robust and reliable decision-making in scenarios where direct simulation is impractical or prohibitively costly.