We introduce Neural Optimal Design of Experiments, a learning-based framework for optimal experimental design in inverse problems that avoids classical bilevel optimization and indirect sparsity regularization. NODE jointly trains a neural reconstruction model and a fixed-budget set of continuous design variables representing sensor locations, sampling times, or measurement angles, within a single optimization loop. By optimizing measurement locations directly rather than weighting a dense grid of candidates, the proposed approach enforces sparsity by design, eliminates the need for l1 tuning, and substantially reduces computational complexity. We validate NODE on an analytically tractable exponential growth benchmark, on MNIST image sampling, and illustrate its effectiveness on a real world sparse view X ray CT example. In all cases, NODE outperforms baseline approaches, demonstrating improved reconstruction accuracy and task-specific performance.

翻译：我们提出了神经最优实验设计，这是一种基于学习的最优实验设计框架，用于反问题，它避免了经典的双层优化和间接稀疏正则化。NODE在一个单一的优化循环中，联合训练一个神经重建模型和一组代表传感器位置、采样时间或测量角度的固定预算连续设计变量。通过直接优化测量位置而非对密集候选网格进行加权，所提方法通过设计强制稀疏性，消除了对l1调参的需求，并显著降低了计算复杂度。我们在一个可解析处理的指数增长基准测试、MNIST图像采样上验证了NODE，并在一个真实世界的稀疏视角X射线CT示例上说明了其有效性。在所有案例中，NODE均优于基线方法，显示出改进的重建精度和特定任务性能。