Surrogate Optimization

Surrogate optimization tackles the challenge of optimizing computationally expensive or complex objective functions by replacing them with simpler, faster-to-evaluate surrogate models. Current research focuses on developing efficient surrogate model architectures, including machine learning approaches and hybrid methods combining machine learning with stochastic or reinforcement learning techniques, to improve the accuracy and efficiency of optimization. These methods are applied across diverse fields, from quantum network design to chemical process optimization, enabling the exploration of larger and more complex problem spaces than previously feasible. The resulting improvements in optimization speed and solution quality have significant implications for various scientific and engineering disciplines.