Surrogate Model
Surrogate models are computationally efficient approximations of complex simulations, primarily used to accelerate optimization, uncertainty quantification, and design exploration in various scientific and engineering fields. Current research emphasizes developing accurate and robust surrogate models using diverse machine learning architectures, including neural networks (e.g., convolutional, recurrent, and graph convolutional networks), Gaussian processes, and normalizing flows, often incorporating techniques like Bayesian optimization and active learning to improve efficiency. The widespread adoption of surrogate models significantly reduces computational costs associated with high-fidelity simulations, enabling faster design iterations, more comprehensive uncertainty analyses, and ultimately, more efficient scientific discovery and technological advancement.
Papers
Attacking deep networks with surrogate-based adversarial black-box methods is easy
Nicholas A. Lord, Romain Mueller, Luca Bertinetto
Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling
Styliani-Christina Fragkouli, Paraskevi Nousi, Nikolaos Passalis, Panagiotis Iosif, Nikolaos Stergioulas, Anastasios Tefas
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis, Michael Kagan, Gregor Kasieczka, Claudius Krause, David Shih, Vinicius Mikuni, Benjamin Nachman, Kevin Pedro, Daniel Winklehner
A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domain
Kairui Bao, Wen Yao, Xiaoya Zhang, Wei Peng, Yu Li