Surrogate Modeling

Surrogate modeling studies how to approximate costly simulators or opaque predictors so they can be queried, optimized, or interpreted more efficiently. Common approaches include Gaussian processes and reduced-order models, neural operators and other deep networks, and hybrid or multi-fidelity methods that combine cheap simulations with a smaller number of accurate ones; active learning can select the most useful new evaluations. Applications range from PDE and climate emulation to engineering design, control, Bayesian optimization, and explanations of black-box models. A central challenge is balancing speed and average prediction accuracy with reliable uncertainty estimates, physical consistency, and performance on unseen regimes or decisions.