Physics-informed surrogate modelling
Engineering challenges increasingly demand models that are both accurate and computationally efficient. Physics-informed surrogate modelling (PISM) combines the strengths of physics and artificial intelligence (AI) to deliver fast, reliable predictions without sacrificing fidelity.
By integrating domain knowledge with data-driven techniques, these models enable real-time analysis, design optimisation and a range of advanced engineering applications. This Mikroniek article explores key methodologies, practical considerations, and lessons learned from industrial applications, offering insights for practitioners and stakeholders seeking to harness the full potential of physics-informed AI for their use cases. It concludes with a brief outlook on future developments in this rapidly evolving field. (Image courtesy of Sioux Technologies)
