  Machine learning techniques are powerful tools for construction of emulators
for complex systems. We explore different machine learning methods and
conceptual methodologies, ranging from functional approximations to dynamical
approximations, to build such emulators for coupled thermal, hydrological,
mechanical and chemical processes that occur near an engineered barrier system
in the nuclear waste repository. Two nonlinear approximators, random forests
and neural networks, are deployed to capture the complexity of the
physics-based model and to identify its most significant hydrological and
geochemical parameters. Our emulators capture the temporal evolution of the
Uranium distribution coefficient of the clay buffer, and identify its
functional dependence on these key parameters. The emulators' accuracy is
further enhanced by assimilating relevant simulated predictors and clustering
strategy. The relative performance of random forests and neural networks shows
the advantage of ensemble learning in random forests algorithm, especially for
highly nonlinear problems with limited data.
