Artificial intelligence and machine learning frameworks have served as computationally efficient mapping between inputs and outputs for engineering problems. These mappings have enabled optimization and analysis routines that have warranted superior designs, ingenious material systems and optimized manufacturing processes. A common occurrence in such modeling endeavors is the existence of multiple source of data, each differentiated by fidelity, operating conditions, experimental conditions, and more. Data fusion frameworks have opened the possibility of combining such differentiated sources into single unified models, enabling improved accuracy and knowledge transfer. However, these frameworks encounter limitations when the different sources are heterogeneous in nature, i.e., not sharing the same input parameter space. These heterogeneous input scenarios can occur when the domains differentiated by complexity, scale, and fidelity require different parametrizations. Towards addressing this void, a heterogeneous multi-source data fusion framework is proposed based on input mapping calibration (IMC) and latent variable Gaussian process (LVGP). In the first stage, the IMC algorithm is utilized to transform the heterogeneous input parameter spaces into a unified reference parameter space. In the second stage, a multi-source data fusion model enabled by LVGP is leveraged to build a single source-aware surrogate model on the transformed reference space. The proposed framework is demonstrated and analyzed on three engineering case studies (design of cantilever beam, design of ellipsoidal void and modeling properties of Ti6Al4V alloy). The results indicate that the proposed framework provides improved predictive accuracy over a single source model and transformed but source unaware model.
Alan Turing Institute（艾伦·图灵研究所）；University Carlos III in Madrid（马德里卡洛斯三世大学）；University of Cambridge（剑桥大学）；Uber AI Labs（优步人工智能实验室）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis
Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation
School of Nursing, The Hong Kong Polytechnic University（香港理工大学护理学院）；School of Software Engineering, South China University of Technology（华南理工大学软件学院）；Department of Computer Science, Hong Kong Baptist University（香港浸会大学计算机科学系）
针对 EHR 与 CXR 采集不同步导致旧胸片影响临床预测的问题，提出 DDL-CXR，利用既往 CXR 和 EHR 时间序列条件化潜在扩散模型生成最新患者特异性表示，并在 MIMIC 上验证其性能优势。
Variable Fusion and Selection via a Spike-and-Slab Approach with Nonlocal Priors
The Institute of Statistical Mathematics（统计数学研究所）；Graduate School of Mathematics, Kyushu University（九州大学大学院数学院）；Faculty of Mathematics, Kyushu University（九州大学数学系）
Meta Fusion: A Unified Framework For Multimodality Fusion with Mutual Learning
Department of Statistics, University of California, Irvine, CA, USA（统计学系，加州大学伊维奇分校）；Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, USA（统计学与应用概率系，加州大学圣巴巴拉分校）
