A Kolmogorov-Arnold Surrogate Model for Chemical Equilibria: Application to Solid Solutions

ArXi:2603.15307v1 Announce Type: new The computational cost of geochemical solvers is a challenging matter. For reactive transport simulations, where chemical calculations are performed up to billions of times, it is crucial to reduce the total computational time. Existing publications have explored various machine-learning approaches to determine the most effective data-driven surrogate model. In particular, multilayer perceptrons are widely employed due to their ability to recognize nonlinear relationships.