Workshop: Machine Learning for Engineering Modeling, Simulation and Design

Probabilistic Adjoint Sensitivity Analysis for Fast Calibration of Partial Differential Equation Models

Jonathan Cockayne · Andrew Duncan


Calibration of large-scale differential equation models to observational or experimental data is a widespread challenge throughout applied sciences and engineering. A crucial bottleneck in state-of-the art calibration methods is the calculation of local sensitivities, i.e. derivatives of the loss function with respect to the estimated parameters, which often necessitates several numerical solves of the underlying system of partial differential equations. In this paper, we present a new probabilistic approach which permits budget-constrained computations of local sensitivities, providing a quantification of uncertainty incurred in the sensitivities from this constraint. Moreover, information from previous sensitivity estimates can be recycled in subsequent computations, reducing the overall computational effort for iterative gradient-based calibration methods.

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