AI RESEARCH
Deep Adaptive Model-Based Design of Experiments
arXiv CS.LG
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ArXi:2603.16146v1 Announce Type: cross 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.