Operator Inference

Operator inference is a data-driven technique for constructing reduced-order models of complex dynamical systems, aiming to create computationally efficient surrogates for high-fidelity models. Current research emphasizes developing robust and accurate methods, focusing on incorporating physical constraints (like energy conservation) into model architectures, handling model uncertainties, and improving performance with limited or noisy data through techniques such as constrained optimization and neural networks. This approach holds significant promise for accelerating simulations in various fields, including fluid dynamics, process engineering, and robotics, enabling faster design and control optimization.