Deep Learning Based Surrogate

Deep learning-based surrogate models aim to replace computationally expensive simulations with faster, more efficient approximations, accelerating scientific discovery and engineering design. Current research focuses on improving the accuracy and efficiency of these surrogates, exploring architectures like convolutional neural networks, graph neural networks, and normalizing flows, often combined with optimization algorithms such as genetic algorithms and Bayesian optimization to enhance exploration of parameter spaces and uncertainty quantification. These advancements are significantly impacting various fields, enabling faster design optimization, improved uncertainty quantification in complex systems (e.g., reservoir simulation, plasma physics), and accelerated drug discovery through more efficient molecular screening.