Efficient Surrogate Model

Efficient surrogate models are computationally inexpensive replacements for complex simulations, aiming to drastically reduce the time and resources needed for tasks like design optimization and uncertainty quantification. Current research focuses on developing and improving various surrogate model architectures, including graph neural operators, physics-informed reduced-order models, and transformers, often incorporating techniques like dimensionality reduction and advanced feature engineering to enhance accuracy and efficiency. These advancements are significantly impacting fields like materials science, fluid dynamics, and engineering design by enabling faster exploration of design spaces and more robust analysis of complex systems.