Novel reduced order modeling strategies for solving stochastic inverse problems

We develop and implement data-driven, projection-based methods for reducing computational demands during the solution of stochastic inverse problems.

^{Coarse mesh FE heat eqn. soln.}

^{Ground truth soln.}

^{POD enriched GFEM coarse mesh solution}

^{Manifold interpolation to uncover reduced order sub-spaces}