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.

翻译：模型驱动实验设计（MBDOE）对于非线性动态系统中的高效参数估计至关重要。然而，传统的自适应MBDOD需要在每个实验步骤之间进行昂贵的后验推断与设计优化，这阻碍了其实时应用。我们通过结合深度自适应设计（DAD）与可微分机理模型来解决这一问题，其中DAD将序贯设计过程摊销为离线训练的神经网络策略。针对已知控制方程但参数不确定的动态系统，我们扩展了序贯对比训练目标以处理干扰参数，并提出了一种基于Transformer的策略架构，该架构能够尊重动态系统的时间结构。我们在四个复杂度递增的系统上验证了该方法：采用Monod动力学的补料分批生物反应器、具有不确定底物抑制的Haldane生物反应器、包含干扰清除参数的两室药代动力学模型，以及适用于实时部署的直流电机。