Neural Network Surrogate

Neural network surrogates are computationally efficient approximations of complex, expensive-to-evaluate models, used to accelerate simulations and optimization processes across diverse scientific and engineering domains. Current research focuses on developing and applying these surrogates, particularly using deep neural networks, graph neural networks, and Bayesian neural networks, often coupled with optimization algorithms like projected gradient descent or evolutionary algorithms, to improve accuracy and efficiency. This approach significantly impacts fields ranging from materials science and engineering design to power grid management and data assimilation, enabling faster simulations, more robust optimization, and improved decision-making in computationally intensive applications.