参数化视角下单模李群上的不确定性传播与贝叶斯融合

Model Fusion with Kullback--Leibler Divergence

We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors and proceeds using a simple assign-and-average approach. The components of the dataset posteriors are assigned to the proposed global model components by solving a regularized variant of the assignment problem. The global components are then updated based on these assignments by their mean under a KL divergence. For exponential family variational distributions, our formulation leads to an efficien…

DAIF：一种基于数据的近似消息传递多模态监督学习中间融合框架

DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing

本研究提出DAIF数据自适应中间融合框架，结合随机矩阵理论与非参数依赖度量，通过近似消息传递生成去噪特征，在模拟及两个多模态数据集上的预测任务中表现优于或媲美现有方法。

基于信任辅助参数化信息融合的分布鲁棒资源分配

Distributionally Robust Resource Allocation with Trust-aided Parametric Information Fusion

针对多数据源参考信息差异下的随机动态资源分配问题，提出基于信任辅助信息融合的分布鲁棒方法，可动态更新信任参数，等价转化为线性规划求解，能提升决策准确性且计算复杂度线性增长。

Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces

Gaussian processes are employed for non-parametric regression in a Bayesian setting. They generalize linear regression, embedding the inputs in a latent manifold inside an infinite-dimensional reproducing kernel Hilbert space. We can augment the inputs with the observations of low-fidelity models in order to learn a more expressive latent manifold and thus increment the model's accuracy. This can be realized recursively with a chain of Gaussian processes with incrementally higher fidelity. We would like to extend these multi-fidelity model realizations to case studies affected by a high-dimens…

GP+：一个基于核学习的高斯过程 Python 库

GP+: A Python Library for Kernel-based learning via Gaussian Processes

本文介绍了基于PyTorch的开源高斯过程库GP+，通过结合非线性流形学习技术与协方差和均值函数，实现了概率数据融合、逆参数估计及混合特征空间建模，并在贝叶斯优化等多场景中展示了其优势。

不确定性下核聚变托卡马克中基于弹丸加料的密度分布控制MPC策略

MPC strategies for density profile control with pellet fueling in nuclear fusion tokamaks under uncertainty

针对ITER托卡马克弹丸加料密度控制的多速率非线性不确定系统问题，提出多阶段MPC方法，通过模型降阶、场景缩减和惩罚项同伦算法降低复杂度，实现了带不确定性处理的实时预测密度控制。