6 citations · 7 across the 6 of their papers we have counts for
6 papers
BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphs
Duyi Pan, Tianao Lou, Xin Li +5
Large Language Models (LLMs) exhibit hallucinations in knowledge-intensive tasks. Graph-based retrieval augmented generation (RAG) has emerged as a promising solution, yet existing…
Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
Haoze Song, Zhihao Li, Mengyi Deng +4
Neural operators (NOs) provide fast, resolution-invariant surrogates for mapping input fields to PDE solution fields, but their predictions can exhibit significant epistemic uncert…
Reformulating Neural Operators in Dimensions for Embedding Evolution
Haoze Song, Zhihao Li, Xiaobo Zhang +3
Neural Operators (NOs) are powerful architectures for learning mappings between function spaces. While most advances focus on refining kernel parameterizations over the -dimensi…
Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries
Zhihao Li, Haoze Song, Di Xiao +2
Partial Differential Equations (PDEs) underpin many scientific phenomena, yet traditional computational approaches often struggle with complex, nonlinear systems and irregular geom…
Parameter estimation of structural dynamics with neural operators enabled surrogate modeling
Mingyuan Zhou, Haoze Song, Wenjing Ye +2
Parameter estimation in structural dynamics generally involves inferring the values of physical, geometric, or even customized parameters based on first principles or expert knowle…
Automated machine learning for physics-informed convolutional neural networks
Wanyun Zhou, Haoze Song, Xiaowen Chu
Recent advances in deep learning for solving partial differential equations (PDEs) have introduced physics-informed neural networks (PINNs), which integrate machine learning with p…