2 citations · 4 across the 14 of their papers we have counts for
15 papers
Gradient-Update Mismatch: Rethinking Conflict-Free Training of Physics-Informed Neural Networks
Jing Xiao, Xinhai Chen, Qinglin Wang +5
Training Physics-Informed Neural Networks (PINNs) requires jointly optimizing physics residual and initial/boundary condition loss terms, which often induce conflicting gradients.…
Audio-Anchored Fusion of Multi-Ratio DiT Reconstruction Residuals for Cross-Domain Audio Deepfake Detection
Haotian Mo, Jie Liu, Siqi Shen +8
Audio deepfake detectors often degrade when generators, corpora, or recording conditions change. We use a Diffusion Transformer (DiT), trained only on bona fide speech, as a frozen…
HistoGPA: A Context-Conditioned Gene-Prior Attention Framework for Histology-Based Spatial Gene Expression Prediction
Ziang Liu, Xinhai Chen, Yigui Feng +3
Predicting spatial gene expression from routine hematoxylin and eosin (H&E) images provides a practical complement to experimental spatial transcriptomics. Existing approaches focu…
Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning
Yunfei Liu, Hao Wang, Yuhang Qi +8
High-fidelity computational fluid dynamics is essential for aerospace design, but engineering-scale simulations of practical three-dimensional aircraft remain computationally expen…
Prior-Guided Symbolic Regression: Towards Scientific Consistency in Equation Discovery
Jing Xiao, Xinhai Chen, Jiaming Peng +7
Symbolic Regression (SR) aims to discover interpretable equations from observational data, with the potential to reveal underlying principles behind natural phenomena. However, exi…
Measuring the Unspoken: A Disentanglement Model and Benchmark for Psychological Analysis in the Wild
Yigui Feng, Qinglin Wang, Haotian Mo +7
Generative psychological analysis of in-the-wild conversations faces two fundamental challenges: (1) existing Vision-Language Models (VLMs) fail to resolve Articulatory-Affective A…
UGM2N: An Unsupervised and Generalizable Mesh Movement Network via M-Uniform Loss
Zhichao Wang, Xinhai Chen, Qinglin Wang +5
Partial differential equations (PDEs) form the mathematical foundation for modeling physical systems in science and engineering, where numerical solutions demand rigorous accuracy-…
AI PsyRoom: Artificial Intelligence Platform for Segmented Yearning and Reactive Outcome Optimization Method
Yigui Feng, Qinglin Wang, Ke Liu +3
Psychological counseling faces huge challenges due to the growing demand for mental health services and the shortage of trained professionals. Large language models (LLMs) have sho…
MeshONet: A Generalizable and Efficient Operator Learning Method for Structured Mesh Generation
Jing Xiao, Xinhai Chen, Qingling Wang +1
Mesh generation plays a crucial role in scientific computing. Traditional mesh generation methods, such as TFI and PDE-based methods, often struggle to achieve a balance between ef…
GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing
Zhichao Wang, Xinhai Chen, Chunye Gong +5
Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of tradi…
3DMeshNet: A Three-Dimensional Differential Neural Network for Structured Mesh Generation
Jiaming Peng, Xinhai Chen, Jie Liu
Mesh generation is a crucial step in numerical simulations, significantly impacting simulation accuracy and efficiency. However, generating meshes remains time-consuming and requir…
Proposing an intelligent mesh smoothing method with graph neural networks
Zhichao Wang, Xinhai Chen, Junjun Yan +1
In CFD, mesh smoothing methods are commonly utilized to refine the mesh quality to achieve high-precision numerical simulations. Specifically, optimization-based smoothing is used…
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving
Junjun Yan, Xinhai Chen, Zhichao Wang +2
Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to cap…
ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations
Junjun Yan, Xinhai Chen, Zhichao Wang +2
Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs),…
An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networks
Xinhai Chen, Jie Liu, Junjun Yan +2
Mesh generation remains a key technology in many areas where numerical simulations are required. As numerical algorithms become more efficient and computers become more powerful, t…