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Xianglong Yu

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3 papers

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EAAI Journal 2026 Journal Article

Review of physics-informed neural networks in hemodynamics

Xianglong Yu
Yu Hu
Rui Guo
Lei Fan
Haiyan Ding
Jingjing Xiao

The circulatory system sustains physiological function through oxygen transport, nutrient delivery, and waste clearance, all of which rely on efficient blood flow. Accurate characterization and quantification of hemodynamics are essential for the diagnosis and treatment of cardiovascular diseases. However, assessing blood flow in a noninvasive and real-time manner remains a major challenge, as current imaging modalities often suffer from limited spatial and temporal resolution, while traditional computational fluid dynamics algorithms are computationally intensive and sensitive to anatomical and physiological uncertainties. Physics-informed neural networks (PINNs), combining physical laws with data-driven learning, provide a promising framework to connect computational modeling with clinical applications. In this review, we provide a comprehensive overview of recent advances in the application of PINNs to hemodynamics. We introduce theoretical foundations, highlight methodological innovations, and discuss applications in simulating blood flow under physiological and pathological conditions, as well as in estimating clinically relevant hemodynamic parameters. Importantly, our analysis highlights that PINNs achieve comparable accuracy to traditional methods while unlocking novel opportunities for patient-specific diagnosis and risk prediction. We conclude with a discussion of the benefits, current limitations, and future directions of PINNs in cardiovascular research, underscoring the transformative potential to accelerate clinical translation through interdisciplinary collaboration.

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AAAI Conference 2025 Conference Paper

Self-Supervised Collaborative Information Bottleneck for Text Readability Assessment

Jinshan Zeng
Xianglong Yu
Xianchao Tong
Wenyan Xiao

Text readability assessment involves categorizing texts based on readers' comprehension levels. Hybrid automatic readability assessment (ARA) models, combining deep and linguistic features, have recently attracted rising attention due to their impressive performance. However, existing hybrid ARA models generally ignore the specific-intrinsic information of deep and linguistic representations, and cannot fully explore their common-intrinsic information. In this paper, we introduce a self-supervised collaborative information bottleneck (SCIB) module for ARA to address these issues. Specifically, we collaboratively consider both specific-intrinsic and common-intrinsic information of the linguistic representation and various levels of deep representations including the document-, sentence- and word-level deep representations, and yield their refined representations via a self-supervised information bottleneck scheme. Extensive experiments are conducted on four English and two Chinese corpora to demonstrate the effectiveness of the proposed model. Experimental results show that the proposed model outperforms state-of-the-art models in terms of four important evaluation metrics, and the suggested SCIB module can effectively capture the specific- and common-intrinsic information.

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AAAI Conference 2024 Conference Paper

InterpretARA: Enhancing Hybrid Automatic Readability Assessment with Linguistic Feature Interpreter and Contrastive Learning

Jinshan Zeng
Xianchao Tong
Xianglong Yu
Wenyan Xiao
Qing Huang

The hybrid automatic readability assessment (ARA) models that combine deep and linguistic features have recently received rising attention due to their impressive performance. However, the utilization of linguistic features is not fully realized, as ARA models frequently concentrate excessively on numerical values of these features, neglecting valuable structural information embedded within them. This leads to limited contribution of linguistic features in these hybrid ARA models, and in some cases, it may even result in counterproductive outcomes. In this paper, we propose a novel hybrid ARA model named InterpretARA through introducing a linguistic interpreter to better comprehend the structural information contained in linguistic features, and leveraging the contrastive learning that enables the model to understand relative difficulty relationships among texts and thus enhances deep representations. Both document-level and segment-level deep representations are extracted and used for the readability assessment. A series of experiments are conducted over four English corpora and one Chinese corpus to demonstrate the effectiveness of the proposed model. Experimental results show that InterpretARA outperforms state-of-the-art models in most corpora, and the introduced linguistic interpreter can provide more useful information than existing ways for ARA.

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