[2603.16146] Deep Adaptive Model-Based Design of Experiments

Statistics > Machine Learning arXiv:2603.16146 (stat) [Submitted on 17 Mar 2026 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Deep Adaptive Model-Based Design of Experiments Authors:Arno Strouwen, Sebastian Micluţa-Câmpeanu View a PDF of the paper titled Deep Adaptive Model-Based Design of Experiments, by Arno Strouwen and 1 other authors View PDF HTML (experimental) Abstract:Model-based design of experiments (MBDOE) is essential for efficient parameter estimation in nonlinear dynamical systems. However, conventional adaptive MBDOE requires costly posterior inference and design optimization between each experimental step, precluding real-time applications. We address this by combining Deep Adaptive Design (DAD), which amortizes sequential design into a neural network policy trained offline, with differentiable mechanistic models. For dynamical systems with known governing equations but uncertain parameters, we extend sequential contrastive training objectives to handle nuisance parameters and propose a transformer-based policy architecture that respects the temporal structure of dynamical systems. We demonstrate the approach on four systems of increasing complexity: a fed-batch bioreactor with Monod kinetics, a Haldane bioreactor with uncertain substrate inhibition, a two-compartment pharmacokinetic model with nuisance clearance parameters, and a DC motor for real-time deployment. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Systems and Contro...