<ul><li>Integration of Sequence Encoding for Online Parameter Identification with Physics-Informed Neural Networks allows for real-time applications with variable parameters, boundary conditions, and initial conditions.</li><li>Architecture employs Deep Sets or Sequence Encoders to encode dynamic parameters, boundary conditions, and initial conditions, enabling the model to adapt to changes in parameters, boundary conditions, and initial conditions.</li><li>Application of this approach to problems such as analyzing the Rossler ODE system, a 2D Navier-Stokes PDE problem with flow past a cylinder, and a 1D heat monitoring problem using real data.</li><li>Demonstration of the model's robustness against noise, ability to generalize, and capability to compute velocity and pressure throughout the domain based on physics-informed neural networks.</li></ul>

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks

Discover more