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Hao Guan

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5 papers

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EAAI Journal 2026 Journal Article

A novel physics-constrained deep learning framework for the inverse design of assembly contact interfaces

Lifei Chen
Qiyin Lin
Mingjun Qiu
Chen Wang
Tao Wang
Hao Guan
Qiyuan Xie
Yuge Jiao

Assembly contact interface characteristics critically influence the performance of precision mechanical systems. Traditional design methods relying on iterative finite element analysis are computationally expensive, while existing deep learning approaches often neglect physical constraints and the complex effects of assembly processes. To address these limitations, this paper proposes a physics-constrained deep learning framework for the inverse design of assembly interfaces. Specifically, we introduce a novel network architecture which integrates multi-source inputs including target contact pressure, assembly parameters, and service conditions. To enforce physical consistency, a differentiable loss function incorporating the impenetrability condition is developed. Furthermore, an optimized learning rate scheduling strategy is implemented to enhance model convergence. Comprehensive ablation and comparative experiments demonstrate that our method outperforms conventional approaches in both accuracy and physical plausibility. When applied to an aero-engine flange structure, the framework enables rapid inverse design of interface morphology, reducing maximum contact pressure by 15. 67% and increasing the effective contact area by 45. 23% compared to traditional designs. This work provides a robust solution for assembly interface design and advances the application of physics-constrained deep learning in complex engineering systems.

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AAMAS Conference 2026 Conference Paper

Automatically Benchmarking LLM Code Agents through Agent-driven Annotation and Evaluation

Lingyue Fu
Bolun Zhang
Hao Guan
Yaoming Zhu
Lin Qiu
Weiwen Liu
Xuezhi Cao
Xunliang Cai

Recent advances in code agents have enabled automated software development at the project level, supported by large language models(LLMs)andwidelyadoptedtools. However, existingbenchmarks for code agent evaluation face two major limitations: high annotation cost and expertise requirements, and rigid evaluation metrics thatrelyprimarilyonunittests. Toaddressthesechallenges, wepropose an agent-driven benchmark construction pipeline that leverages human supervision to efficiently generate diverse and challenging project-level tasks. Based on this approach, we introduce PRDBench, a novel benchmark comprising 50 real-world Python projects across 20 domains, each with structured Product Requirement Document (PRD) requirements, comprehensive evaluation criteria, and reference implementations. PRDBench features rich data sources, high task complexity, and flexible metrics. We further employ an Agent-as-a-Judge paradigm to score agent outputs, enabling the evaluation of various test types beyond unit tests. Extensive experiments on PRDBench demonstrate its effectiveness in assessing the capabilities of both code agents and evaluation agents, providing a scalable and robust framework for annotation and evaluation.

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JBHI Journal 2023 Journal Article

Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline With Structural MRI

Hao Guan
Ling Yue
Pew-Thian Yap
Shifu Xiao
Andrea Bozoki
Mingxia Liu

Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer's disease (AD) which happens even earlier than mild cognitive impairment (MCI). Progressive SCD will convert to MCI with the potential of further evolving to AD. Therefore, early identification of progressive SCD with neuroimaging techniques ( e. g. , structural MRI) is of great clinical value for early intervention of AD. However, existing MRI-based machine/deep learning methods usually suffer the small-sample-size problem and lack interpretability. To this end, we propose an interpretable autoencoder model with domain transfer learning (IADT) for progression prediction of SCD. Firstly, the proposed model can leverage MRIs from both the target domain (i. e. , SCD) and auxiliary domains ( e. g. , AD and NC) for progressive SCD identification. Besides, it can automatically locate the disease-related brain regions of interest (defined in brain atlases) through an attention mechanism, which shows good interpretability. In addition, the IADT model is straightforward to train and test with only 5 $\sim$ 10 seconds on CPUs and is suitable for medical tasks with small datasets. Extensive experiments on the publicly available ADNI dataset and a private CLAS dataset have demonstrated the effectiveness of the proposed method.

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YNIMG Journal 2023 Journal Article

DomainATM: Domain adaptation toolbox for medical data analysis

Hao Guan
Mingxia Liu

Domain adaptation (DA) is an important technique for modern machine learning-based medical data analysis, which aims at reducing distribution differences between different medical datasets. A proper domain adaptation method can significantly enhance the statistical power by pooling data acquired from multiple sites/centers. To this end, we have developed the Domain Adaptation Toolbox for Medical data analysis (DomainATM) - an open-source software package designed for fast facilitation and easy customization of domain adaptation methods for medical data analysis. The DomainATM is implemented in MATLAB with a user-friendly graphical interface, and it consists of a collection of popular data adaptation algorithms that have been extensively applied to medical image analysis and computer vision. With DomainATM, researchers are able to facilitate fast feature-level and image-level adaptation, visualization and performance evaluation of different adaptation methods for medical data analysis. More importantly, the DomainATM enables the users to develop and test their own adaptation methods through scripting, greatly enhancing its utility and extensibility. An overview characteristic and usage of DomainATM is presented and illustrated with three example experiments, demonstrating its effectiveness, simplicity, and flexibility. The software, source code, and manual are available online.

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YNIMG Journal 2021 Journal Article

MRI-based Alzheimer’s disease prediction via distilling the knowledge in multi-modal data

Hao Guan
Chaoyue Wang
Dacheng Tao

Mild cognitive impairment (MCI) conversion prediction, i.e., identifying MCI patients of high risks converting to Alzheimer's disease (AD), is essential for preventing or slowing the progression of AD. Although previous studies have shown that the fusion of multi-modal data can effectively improve the prediction accuracy, their applications are largely restricted by the limited availability or high cost of multi-modal data. Building an effective prediction model using only magnetic resonance imaging (MRI) remains a challenging research topic. In this work, we propose a multi-modal multi-instance distillation scheme, which aims to distill the knowledge learned from multi-modal data to an MRI-based network for MCI conversion prediction. In contrast to existing distillation algorithms, the proposed multi-instance probabilities demonstrate a superior capability of representing the complicated atrophy distributions, and can guide the MRI-based network to better explore the input MRI. To our best knowledge, this is the first study that attempts to improve an MRI-based prediction model by leveraging extra supervision distilled from multi-modal information. Experiments demonstrate the advantage of our framework, suggesting its potentials in the data-limited clinical settings.

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