发表机构
Bilkent University; Texas A&M University（比尔肯特大学; 德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对昂贵随机模拟器无梯度接口的反问题，提出MUTACO序贯设计方法，仅用噪声前向评估匹配多分量响应，在有限预算下实现最低响应误差和较低参数误差。
AI 中文摘要
许多物理反问题由昂贵的随机模拟器介导，这些模拟器无法提供响应梯度、伴随灵敏度或可微分的模拟器接口。我们提出了基于多目标的置信采集（MUTACO），一种不确定性感知的序贯设计方法，用于响应匹配，仅需噪声前向评估，并同时学习多分量响应及其预测不确定性。我们针对随机相位振荡器的无序网络开发并评估了MUTACO。反演任务是在多种驱动条件下，从宏观旋转和同步曲线推断局部势和相互作用的统计特性。MUTACO不要求唯一恢复这些统计特性，而是搜索参数配置，其预测的旋转和同步响应与目标响应兼容，同时考虑预测不确定性。这种以响应为中心的表述适用于实验可观测的观测量表征系综统计而非特定微观实现的情况。在严格有限的模拟预算下，MUTACO在测试的自适应搜索、空间填充和基于模拟的推断方法中实现了最低的留出响应误差，同时参数恢复误差也低于对比方法。这些结果表明，响应目标的序贯设计是一种从有限噪声前向评估中进行反演建模的模拟器高效方法，并暗示了对具有相关多分量响应和弱约束参数方向的随机物理系统的更广泛适用性。
英文摘要
Many physical inverse problems are mediated by expensive stochastic simulators for which response gradients, adjoint sensitivities, or a differentiable simulator interface are unavailable. We introduce Multiple Target-based Confident Acquisition (MUTACO), an uncertainty-aware sequential-design method for response matching that requires only noisy forward evaluations and learns the multicomponent response together with its predictive uncertainty. We develop and evaluate MUTACO for a disordered network of stochastic phase oscillators. The inverse task is to infer statistical properties of the local potential and interactions from macroscopic rotation and synchronization curves under multiple driving conditions. Rather than requiring unique recovery of these statistical properties, MUTACO searches for parameter configurations whose predicted rotation and synchronization responses are compatible with the target response while accounting for predictive uncertainty. This response-centered formulation is appropriate when experimentally accessible observables characterize ensemble statistics rather than a particular microscopic realization. Under strictly limited simulation budgets, MUTACO achieves the lowest held-out response error among the tested adaptive-search, space-filling, and simulation-based inference methods, while also yielding lower parameter-recovery error than the compared methods. These results demonstrate that response-targeted sequential design is a simulator-efficient approach to inverse modeling from finite noisy forward evaluations and suggest broader applicability to stochastic physical systems with correlated multicomponent responses and weakly constrained parameter directions.
Comments28 pages, 12 figures, 5 tables
