用能量模型建模参数与解的联合分布，实现低成本高效传感器选址。
Deep Optimal Sensor Placement for Black Box Stochastic Simulations
- 构建参数与解的联合能量模型，学习函数化表示。
- 在多种随机问题上验证，比传统方法更高效且信息量更高。
- 可插拔式替代品，支持任意点条件调节，适合复杂系统优化。
针对黑箱随机系统中成本有效的参数推断传感器配置选择，现有方法面临显著计算障碍。本文提出一种新颖且稳健的方法，通过联合能量模型对输入参数与解的联合分布进行建模，并基于仿真数据训练。与传统仿真推断方法需依赖特定点采样不同，本方法学习参数与解的函数化表示，作为分辨率无关的即插即用代理模型，可对任意点集进行条件化，从而实现高效的传感器选址。我们在多种随机问题上验证了该框架的有效性，结果表明，相较于传统方法，本方法能在更低计算成本下提供更具信息量的传感器位置。
原文摘要 · Abstract (English)
Selecting cost-effective optimal sensor configurations for subsequent inference of parameters in black-box stochastic systems faces significant computational barriers. We propose a novel and robust approach, modelling the joint distribution over input parameters and solution with a joint energy-based model, trained on simulation data. Unlike existing simulation-based inference approaches, which must be tied to a specific set of point evaluations, we learn a functional representation of parameters and solution. This is used as a resolution-independent plug-and-play surrogate for the joint distribution, which can be conditioned over any set of points, permitting an efficient approach to sensor placement. We demonstrate the validity of our framework on a variety of stochastic problems, showing that our method provides highly informative sensor locations at a lower computational cost compared to conventional approaches.
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