Multiphysics-informed learning algorithm for vadose zone transport modeling

Abstract:

Combining multiple transport measurement types (pressure head, temperature, concentration) in a multiphysics framework is known to improve parameter estimates through crossover of mutual information. While crossover effects benefit the estimation process, there remain challenges associated with time and number of iterations to convergence, uncertainty in parameter estimates and resources required for field-scale multiphysics simulations. To overcome these challenges, we develop and test a multiphysics-informed learning algorithm that performs the joint solution of explicitly coupled variably-saturated water, heat, and solute transport equations with random forest (RF) and ensemble gradient boosting (EGB) regression machine learning kernels. These kernels provide simulated transport measurements based on stochastic input that regularizes the estimation process through additional pressure head (RF) and temperature and concentration (EGB) terms in the objective function. Our results demonstrate that the multiphysics-informed learning algorithm (1) reduces the number of iterations required for convergence by one order of magnitude, (2) reduces the error in estimated (water, heat, and solute) transport parameters by one order of magnitude, and (3) provides reduced-order machine learning models for coupled time-dependent simulations of pressure head, temperature, and solute concentration thereby avoiding the need for computational resources associated with traditional numerical forward solutions. We envision that reduced-order models will provide a rapid means to inform field management and policy decisions under stochastic boundary conditions.