Numerical Simulation

Numerical simulation, a cornerstone of scientific inquiry and engineering design, aims to efficiently model complex systems and processes using computational methods. Current research emphasizes developing faster and more accurate surrogate models, often employing machine learning techniques like neural networks (including physics-informed neural networks, graph neural networks, and transformers), Bayesian optimization, and polynomial chaos expansions, to replace or augment computationally expensive traditional simulations. This accelerates scientific discovery and enables optimization across diverse fields, from materials science and fluid dynamics to climate modeling and geological carbon storage, by significantly reducing computational costs and expanding the feasible parameter space for exploration.