用神经网络策略实现动态系统实验设计的实时优化。
Deep Adaptive Model-Based Design of Experiments
- 将序列实验设计转化为可离线训练的神经网络策略。
- 在4个复杂系统上实现秒级实时设计，比传统方法快100倍以上。
- 适合需要快速迭代的生物、药理和控制系统研发人员。
基于模型的实验设计（MBDOE）对非线性动态系统中的参数高效估计至关重要。然而，传统自适应MBDOE需在每步实验后进行昂贵的后验推断与设计优化，难以实现实时应用。本文结合深度自适应设计（DAD），将序列设计过程转化为离线训练的神经网络策略，并引入可微分机制模型。针对具有已知动力学方程但参数不确定的系统，扩展了序列对比学习目标以处理无关参数，并提出一种尊重时间结构的Transformer架构。在四个复杂度递增的系统上验证：具有Monod动力学的连续培养生物反应器、存在不确定底物抑制的Haldane反应器、含无关清除率参数的两室药代动力学模型，以及用于实时部署的直流电机系统。
原文摘要 · Abstract (English)
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.
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