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Large Language Models (LLMs) have demonstrated significant potential across various applications, but their use as AI copilots in complex and specialized tasks is often hindered by AI hallucinations, where models generate outputs that seem plausible but are incorrect. To address this challenge, we develop AutoFEA, an intelligent system that integrates LLMs with Finite Element Analysis (FEA) to automate the generation of FEA input files. Our approach features a novel planning method and a graph convolutional network (GCN)-Transformer Link Prediction retrieval model, which enhances the accuracy and reliability of the generated simulations. The AutoFEA system proceeds with key steps: dataset preparation, step-by-step planning, GCN-Transformer Link Prediction retrieval, LLM-driven code generation, and simulation using CalculiX. In this workflow, the GCN-Transformer model predicts and retrieves relevant example codes based on relationships between different steps in the FEA process, guiding the LLM in generating accurate simulation codes. We validate AutoFEA using a specialized dataset of 512 meticulously prepared FEA projects, which provides a robust foundation for training and evaluation. Our results demonstrate that AutoFEA significantly reduces AI hallucinations by grounding LLM outputs in physically accurate simulation data, thereby improving the success rate and accuracy of FEA simulations and paving the way for future advancements in AI-assisted engineering tasks. 

Collaborative filtering (CF) is a prevalent technique utilized in recommender systems (RSs), and has been extensively deployed in various real-world applications. A recent study in CF focuses on improving the quality of representations from the perspective of alignment and uniformity on the hyperspheres for enhanced recommendation performance. It promotes alignment to increase the similarity between representations of interacting users and items, and enhances uniformity to have more uniformly distributed user and item representations within their respective hyperspheres. However, although alignment and uniformity are enforced by two different optimized objectives, respectively, they jointly constitute the supervised signals for model training. Models trained with only supervised signals in labeled data can inevitably overfit the noise introduced by label sampling variance, even with i.i.d. datasets. This overfitting to noise further compromises the model's generalizability and performance on unseen testing data. To address this issue, in this study, we aim to mitigate the effect caused by the sampling variance in labeled training data to improve representation generalizability from the perspective of alignment and uniformity. Representations with more generalized alignment and uniformity further lead to improved model performance on testing data. Specifically, we model the data as a user-item interaction bipartite graph, and apply a graph neural network (GNN) to learn the user and item representations. This graph modeling approach allows us to integrate self-supervised signals into the RS, by performing self-supervised contrastive learning on the user and item representations from the perspective of label-irrelevant alignment and uniformity. Since the representations are less dependent on label supervision, they can capture more label-irrelevant data structures and patterns, leading to more generalized alignment and uniformity. We conduct extensive experiments on three benchmark datasets to demonstrate the superiority of our framework (i.e., improved performance and faster convergence speed). Our codes: https://github.com/zyouyang/AUPlus