Input Output

Input-output analysis focuses on understanding and modeling the relationships between inputs and outputs in various systems, aiming to predict outputs based on inputs or to design systems with desired input-output behaviors. Current research emphasizes developing robust methods for handling noisy data, incorporating prior knowledge into model architectures (like neural networks with structured Jacobians), and applying these techniques to diverse domains, including economic modeling (e.g., using input-output tables and machine learning for sustainability analysis), control systems (e.g., synthesizing controllers from noisy data), and code generation (e.g., using large language models guided by input-output specifications). These advancements improve the accuracy, reliability, and applicability of input-output models across numerous scientific and engineering disciplines.