Adjoint-Compatible Surrogates of the Expected Information Gain for Optimal Experimental Design

TL;DR

This paper introduces adjoint-compatible surrogate criteria for the expected information gain in optimal experimental design, enabling efficient Bayesian parameter estimation in complex dynamical systems.

Contribution

It proposes novel surrogate methods for EIG that are compatible with adjoint-based control and effective for non-Gaussian or multimodal priors.

Findings

Surrogates are competitive in nearly Gaussian regimes.

Gaussian tilting surrogate is exact in linear-Gaussian cases.

Surrogates outperform Fisher-based designs with complex priors.

Abstract

We consider optimal experimental design for parameter estimation in dynamical systems governed by controlled ordinary differential equations. In such problems, Fisher-based criteria are attractive because they lead to time-additive objectives compatible with adjoint-based optimal control, but they remain intrinsically local and may perform poorly under strong nonlinearities or non-Gaussian prior uncertainty. By contrast, the expected information gain (EIG) provides a principled Bayesian objective, yet it is typically too costly to evaluate and does not naturally admit an adjoint-compatible formulation. In this work, we introduce adjoint-compatible surrogates of the EIG based on an exact chain-rule decomposition and tractable approximations of the posterior distribution of the unknown parameter. This leads to two surrogate criteria: an instantaneous surrogate, obtained by replacing the posterior with the prior, and a Gaussian tilting surrogate, obtained by reweighting the prior through a design-driven quadratic information factor. We also propose a multi-center tilting surrogate to improve robustness for complex or multimodal priors. We establish theoretical properties of these surrogates, including exactness of the Gaussian tilting surrogate in the linear-Gaussian setting, and illustrate their behavior on benchmark controlled dynamical systems. The results show that the proposed surrogates remain competitive in nearly Gaussian regimes and provide clearer benefits over Fisher-based designs when prior uncertainty is non-Gaussian or multimodal.