Probabilistic Gradients for Fast Calibration of Differential Equation Models

@article{Cockayne2021ProbabilisticGF,
  title={Probabilistic Gradients for Fast Calibration of Differential Equation Models},
  author={Jon Cockayne and Andrew B. Duncan},
  journal={ArXiv},
  year={2021},
  volume={abs/2009.04239}
}
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 or ordinary differential equations. In this paper we present a… 
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