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Qian Sun

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8 papers

1 author row

EAAI Journal 2026 Journal Article

Ensemble Kalman filter-driven adaptive modeling for real-time fracturing pressure forecasting

Zhengxin Zhang
Lei Hou
Qian Sun
Mao Sheng
Fengshou Zhang
Tingxue Jiang
Xiaobing Bian
Jiangfeng Luo

Accurately forecasting fracturing pressure significantly enhances the safety and efficiency of hydraulic fracturing design. Traditional machine learning methods rely on offline datasets for model training, making it challenging to accurately capture the dynamic variations in features under real-time conditions. This study presents a data assimilation-driven workflow for predicting fracturing pressure, enabling real-time capture of feature variations and adaptive model updates. By integrating the Gated Recurrent Unit (GRU) with the Ensemble Kalman Filter (EnKF), this study develops and evaluates three updating strategies: updating the GRU model parameters, updating the GRU model hidden states, and updating both the GRU model parameters and the hidden state simultaneously. Sensitivity analyses were conducted on two key parameters—process noise and ensemble size. The results demonstrate that the approach of adjusting the GRU model parameters, with a process noise of 0. 01 and an ensemble size of 50, delivers optimal performance. The optimal configuration was adopted for subsequent case studies, where improved pressure prediction accuracy in the first case and optimized fracturing design in the second confirmed the workflow's overall effectiveness. The EnKF-updated predictions reduced Symmetric Mean Absolute Percentage Error (SMAPE) from 3. 56%-6. 48% to 0. 90%-3. 02% and Root Mean Squared Error (RMSE) from 3. 51 to 6. 19 to 1. 05-2. 80, outperforming direct GRU model predictions. Furthermore, the optimized fracturing design increased the cumulative proppant volume from 124. 96 cubic meters (m3) to 147. 89 m3, a 18. 35% improvement. By capturing real-time feature variations, this new workflow provides a robust solution for improving pressure prediction accuracy, thereby enabling optimization of fracturing designs.

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AAAI Conference 2026 Conference Paper

Training-Free ANN-to-SNN Conversion for High-Performance Spiking Transformers

Jingya Wang
Xin Deng
Wenjie Wei
Dehao Zhang
Shuai Wang
Qian Sun
Jieyuan Zhang
Hanwen Liu

Leveraging the event-driven paradigm, Spiking Neural Networks (SNNs) offer a promising approach for constructing energy-efficient Transformer architectures. Compared to directly trained Spiking Transformers, ANN-to-SNN conversion methods bypass the high training costs. However, existing methods still suffer from notable limitations, failing to effectively handle nonlinear operations in Transformer architectures and requiring additional fine-tuning processes for pre-trained ANNs. To address these issues, we propose a high-performance and training-free ANN-to-SNN conversion framework tailored for Transformer architectures. Specifically, we introduce a Multi-basis Exponential Decay (MBE) neuron, which employs an exponential decay strategy and multi-basis encoding method to efficiently approximate various nonlinear operations. It removes the requirement for weight modifications in pre-trained ANNs. Extensive experiments across diverse tasks (CV, NLU, NLG) and mainstream Transformer architectures (ViT, RoBERTa, GPT-2) demonstrate that our method achieves near-lossless conversion accuracy with significantly lower latency. This provides a promising pathway for the efficient and scalable deployment of Spiking Transformers in real-world applications.

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EAAI Journal 2025 Journal Article

Characterizing the hydrodynamic and mechanical properties of hydraulic fractured shale plays using a Kolmogorov-Arnold-Network-assisted data assimilation approach

Ziqiang Zhou
Baojiang Sun
Qian Sun

The effectiveness of hydraulic fracture stimulation is crucial for developing shale hydrocarbon reservoirs. Geophysical-based monitoring tools and numerical simulators require high capital and computational investments, making data-driven models preferred for shale reservoir characteristics. This study integrates the Kolmogorov-Arnold Networks (KAN) with a data assimilation algorithm, Ensemble Kalman Filter (EnKF), and structures a KAN-assisted-EnKF (K-EnKF) protocol. It characterizes the critical rock mechanical and hydrodynamical properties using the real-time pumping pressure as input. The KAN model predicts time-series pump pressure and hydraulic fracture characteristic data for specific stimulation stages. The data assimilation efficacy is significantly enhanced by employing the KAN model as the predictor model. Blind testing applications indicate that KAN outperforms Deep Neural Networks (DNN) by more than an order of magnitude fewer learnable parameters. The numerical experiments show that the EnKF model, when coupled with a KAN containing 1068 learnable parameters, achieves a Mean Squared Error (MSE) of 0. 0313 in matching field-measured pump pressure data. The DNN assisted EnKF model (DNN-EnKF) requires 12, 869 learnable parameters to achieve a similar pressure matching quality with an MSE of 0. 04 without the occurrence of overlearning. Moreover, the KAN model enhances the computational efficiency of EnKF by over twenty times when the numerical simulator is employed as the predictor. The statistical analysis of the inversion results reveals that the proposed K-EnKF model yields hydrodynamic and rock mechanical properties with significantly lower uncertainty compared to the DNN-EnKF model, which consistently generates multiple solutions following a Gaussian distribution.

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NeurIPS Conference 2025 Conference Paper

Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks

Jieyuan (Eric) Zhang
Xiaolong Zhou
Shuai Wang
Wenjie Wei
Hanwen Liu
Qian Sun
Malu Zhang
Yang Yang

Spiking Neural Networks (SNNs) demonstrate significant potential for energy-efficient neuromorphic computing through an event-driven paradigm. While training methods and computational models have greatly advanced, SNNs struggle to achieve competitive performance in visual long-sequence modeling tasks. In artificial neural networks, the effective receptive field (ERF) serves as a valuable tool for analyzing feature extraction capabilities in visual long-sequence modeling. Inspired by this, we introduce the Spatio-Temporal Effective Receptive Field (ST-ERF) to analyze the ERF distributions across various Transformer-based SNNs. Based on the proposed ST-ERF, we reveal that these models suffer from establishing a robust global ST-ERF, thereby limiting their visual feature modeling capabilities. To overcome this issue, we propose two novel channel-mixer architectures: \underline{m}ulti-\underline{l}ayer-\underline{p}erceptron-based m\underline{ixer} (MLPixer) and \underline{s}plash-and-\underline{r}econstruct \underline{b}lock (SRB). These architectures enhance global spatial ERF through all timesteps in early network stages of Transformer-based SNNs, improving performance on challenging visual long-sequence modeling tasks. Extensive experiments conducted on the Meta-SDT variants and across object detection and semantic segmentation tasks further validate the effectiveness of our proposed method. Beyond these specific applications, we believe the proposed ST-ERF framework can provide valuable insights for designing and optimizing SNN architectures across a broader range of tasks. The code is available at \href{https: //github. com/EricZhang1412/Spatial-temporal-ERF}{\faGithub~EricZhang1412/Spatial-temporal-ERF}.

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EAAI Journal 2023 Journal Article

Transfer learning for cross-scene 3D pavement crack detection based on enhanced deep edge features

Rong Gui
Qian Sun
Wenqing Wu
Dejin Zhang
Qingquan Li

Inspired by heterogeneity of rapid-increasing 3D pavement data and the generalization ability of transfer learning, a robust and generalized framework for cross-scene 3D pavement-crack detection and attribute extraction was proposed in this paper, called profile component decomposition model with holistically nested edge detection (PCDM-HED). The core purpose of the PCDM-HED is to construct the enhanced deep edge features. By applying profile frequency, sparse characteristics of 3D profiles, and fusing the multiscale and multilevel edge characteristics of 3D depth maps in HED network, the robust enhanced feature can highlight the essential properties of cracks in heterogeneous 3D data. It overcomes the complex domain shifts caused by different 3D imaging conditions, pavement textures, and crack distributions in heterogeneous data. Cross-domain transfer experiments were carried out over seven 3D/2D datasets with 915 pavement sections. The results show that proposed PCDM-HED achieved average buffered Hausdorff scores of 90. 17 to 96. 42, recall scores of 0. 84 to 0. 91, and F-values of 0. 85 to 0. 89 in six different datasets without labeled samples. Compared with 9 groups of comparison results, including the traditional and related state-of-the-art methods, the transfer generalization effect of proposed PCDM-HED is more than 23% higher than that of comparison results. The proposed PCDM-HED makes full use of limited off-the-shelf samples, demonstrated strong transfer learning capability. It provides an effective solution for heterogeneous 3D pavement-crack detection tasks in engineering, in the case of limited labeled samples or even no corresponding labeled samples.

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YNICL Journal 2019 Journal Article

Disturbed neurovascular coupling in type 2 diabetes mellitus patients: Evidence from a comprehensive fMRI analysis

Bo Hu
Lin-Feng Yan
Qian Sun
Ying Yu
Jin Zhang
Yu-Jie Dai
Yang Yang
Yu-Chuan Hu

BACKGROUND: Previous studies presumed that the disturbed neurovascular coupling to be a critical risk factor of cognitive impairments in type 2 diabetes mellitus (T2DM), but distinct clinical manifestations were lacked. Consequently, we decided to investigate the neurovascular coupling in T2DM patients by exploring the MRI relationship between neuronal activity and the corresponding cerebral blood perfusion. METHODS: Degree centrality (DC) map and amplitude of low-frequency fluctuation (ALFF) map were used to represent neuronal activity. Cerebral blood flow (CBF) map was used to represent cerebral blood perfusion. Correlation coefficients were calculated to reflect the relationship between neuronal activity and cerebral blood perfusion. RESULTS: At the whole gray matter level, the manifestation of neurovascular coupling was investigated by using 4 neurovascular biomarkers. We compared these biomarkers and found no significant changes. However, at the brain region level, neurovascular biomarkers in T2DM patients were significantly decreased in 10 brain regions. ALFF-CBF in left hippocampus and fractional ALFF-CBF in left amygdala were positively associated with the executive function, while ALFF-CBF in right fusiform gyrus was negatively related to the executive function. The disease severity was negatively related to the memory and executive function. The longer duration of T2DM was related to the milder depression, which suggests T2DM-related depression may not be a physiological condition but be a psychological condition. CONCLUSION: Correlations between neuronal activity and cerebral perfusion maps may be a method for detecting neurovascular coupling abnormalities, which could be used for diagnosis in the future. Trial registry number: This study has been registered in ClinicalTrials.gov (NCT02420470) on April 2, 2015 and published on July 29, 2015.

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YNIMG Journal 2019 Journal Article

Neurovascular decoupling in type 2 diabetes mellitus without mild cognitive impairment: Potential biomarker for early cognitive impairment

Ying Yu
Lin-Feng Yan
Qian Sun
Bo Hu
Jin Zhang
Yang Yang
Yu-Jie Dai
Wu-Xun Cui

Type 2 diabetes mellitus (T2DM) is a significant risk factor for mild cognitive impairment (MCI) and the acceleration of MCI to dementia. The high glucose level induce disturbance of neurovascular (NV) coupling is suggested to be one potential mechanism, however, the neuroimaging evidence is still lacking. To assess the NV decoupling pattern in early diabetic status, 33 T2DM without MCI patients and 33 healthy control subjects were prospectively enrolled. Then, they underwent resting state functional MRI and arterial spin labeling imaging to explore the hub-based networks and to estimate the coupling of voxel-wise cerebral blood flow (CBF)-degree centrality (DC), CBF-mean amplitude of low-frequency fluctuation (mALFF) and CBF- mean regional homogeneity (mReHo). We further evaluated the relationship between NV coupling pattern and cognitive performance (false discovery rate corrected). T2DM without MCI patients displayed significant decrease in the absolute CBF-mALFF, CBF-mReHo coupling of CBFnetwork and in the CBF-DC coupling of DCnetwork. Besides, networks which involved CBF and DC hubs mainly located in the default mode network (DMN). Furthermore, less severe disease and better cognitive performance in T2DM patients were significantly correlated with higher coupling of CBF-DC, CBF-mALFF or CBF-mReHo, especially for the cognitive dimensions of general function and executive function. Thus, coupling of CBF-DC, CBF-mALFF and CBF-mReHo may serve as promising indicators to reflect NV coupling state and to explain the T2DM related early cognitive impairment.

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NeurIPS Conference 2011 Conference Paper

A Two-Stage Weighting Framework for Multi-Source Domain Adaptation

Qian Sun
Rita Chattopadhyay
Sethuraman Panchanathan
Jieping Ye

Discriminative learning when training and test data belong to different distributions is a challenging and complex task. Often times we have very few or no labeled data from the test or target distribution but may have plenty of labeled data from multiple related sources with different distributions. The difference in distributions may be in both marginal and conditional probabilities. Most of the existing domain adaptation work focuses on the marginal probability distribution difference between the domains, assuming that the conditional probabilities are similar. However in many real world applications, conditional probability distribution differences are as commonplace as marginal probability differences. In this paper we propose a two-stage domain adaptation methodology which combines weighted data from multiple sources based on marginal probability differences (first stage) as well as conditional probability differences (second stage), with the target domain data. The weights for minimizing the marginal probability differences are estimated independently, while the weights for minimizing conditional probability differences are computed simultaneously by exploiting the potential interaction among multiple sources. We also provide a theoretical analysis on the generalization performance of the proposed multi-source domain adaptation formulation using the weighted Rademacher complexity measure. Empirical comparisons with existing state-of-the-art domain adaptation methods using three real-world datasets demonstrate the effectiveness of the proposed approach.

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