Response Surface

Response surface methodology focuses on modeling the relationship between input variables and a system's output, aiming to optimize the output or understand its sensitivity to changes in inputs. Current research emphasizes efficient surrogate model construction using techniques like polynomial chaos expansion and machine learning, often incorporating advanced sampling strategies and transfer learning to improve accuracy and reduce computational cost. These advancements are crucial for optimizing complex systems across diverse fields, from engineering design and materials science to marketing and advertising, where direct experimentation is expensive or impractical. The development of robust and efficient response surface methods continues to be a significant area of investigation, driving progress in both theoretical understanding and practical applications.