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
Neuro-symbolic partial differential equation solver
Pouria Mistani, Samira Pakravan, Rajesh Ilango, Sanjay Choudhry, Frederic Gibou
An adaptive multi-fidelity sampling framework for safety analysis of connected and automated vehicles
Xianliang Gong, Shuo Feng, Yulin Pan
Online model error correction with neural networks in the incremental 4D-Var framework
Alban Farchi, Marcin Chrust, Marc Bocquet, Patrick Laloyaux, Massimo Bonavita
Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System
M. Rahman, Abid Khan, Sayeed Anowar, Md Al-Imran, Richa Verma, Dinesh Kumar, Kazuma Kobayashi, Syed Alam
Convergence of weak-SINDy Surrogate Models
Benjamin Russo, M. Paul Laiu
Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations
Tushar, Souvik Chakraborty
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference
Giovanni Charles, Timothy M. Wolock, Peter Winskill, Azra Ghani, Samir Bhatt, Seth Flaxman