# Deep Adaptive Model-Based Design of Experiments Source: [Deep Adaptive Model-Based Design of Experiments]() Collection index: [llms.txt]() Data updated: 2026-03-26T05:00:12.044Z Authors: Arno Strouwen, Sebastian Miclu\\c{t}a-C\\^ampeanu Published: 2026-03-17T05:53:09.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Adaptive experimental design on DC motor](): Adaptive experimental design; DC motor; sPCE: 2.96. ## Original sources and implementation [Original paper]() [PDF]()