Statistical Simulator for the Engine Knock

期望多样化效用（EDU）：昂贵计算机模拟器的多样化贝叶斯优化

Expected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulators

本文提出期望多样化效用（EDU）方法，用于贝叶斯优化中以搜索多样化的局部最优解，并在高斯过程代理模型下推导出闭式采集函数，揭示探索-利用-多样性权衡，在数值实验和实际应用中均展现出优于现有方法的性能。

Black-Box Optimization with Local Generative Surrogates

We propose a novel method for gradient-based optimization of black-box simulators using differentiable local surrogate models. In fields such as physics and engineering, many processes are modeled with non-differentiable simulators with intractable likelihoods. Optimization of these forward models is particularly challenging, especially when the simulator is stochastic. To address such cases, we introduce the use of deep generative models to iteratively approximate the simulator in local neighborhoods of the parameter space. We demonstrate that these local surrogates can be used to approximate…

复杂动态模拟器的快速且精确的仿真

Fast and Accurate Emulation of Complex Dynamic Simulators

本文提出一种基于高斯过程和单步前向框架的快速精确动态模拟器仿真方法，通过链接GP实现后验均值和方差的精确计算，避免了昂贵的蒙特卡洛近似，大幅提升计算效率并保持预测精度。

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remain limited because of the impracticability of rewriting complex scientific simulators in a PPL, the computational cost of inference, and the lack of scalable implementations. To address these, we present a novel PPL framework that couples directly to existing scientific simulators through a cross-platform probabilistic execution protocol and provides Markov chain Monte Carlo (MCMC) and deep-learning-based inference co…

利用昂贵模拟器贝叶斯推理中的梯度信息

Exploiting Gradients in Bayesian Inference of Expensive Simulators

本文将梯度信息融入高斯过程代理，在有限模拟预算下加速昂贵模拟器的贝叶斯推理，反向模式微分下效率提升显著，适用于参数数超输出维度的场景。