@article{wang2026couplingphaseseparationgeometry,title={Coupling between Phase Separation and Geometry on a Closed Elastic Curve: Free Energy Minimization and Dynamics},author={Wang, Hanchun and Adhikari, Ronojoy and Cates, Michael E.},journal={arXiv preprint arXiv:2602.22977},year={2026},archiveprefix={arXiv},primaryclass={cond-mat.soft},doi={10.48550/arXiv.2602.22977},url={https://arxiv.org/abs/2602.22977}}
Cell Insight
Tuning the State: Matching Condensate Material Properties with Physiological Demands and Pathological Dysfunction
Jingxuan Luo, Zhehao Li, Ziyin Shen, and 6 more authors
@article{luo2026tuningstate,title={Tuning the State: Matching Condensate Material Properties with Physiological Demands and Pathological Dysfunction},author={Luo, Jingxuan and Li, Zhehao and Shen, Ziyin and Zhai, Xiaoyu and Chen, Jiayue and Wang, Hanchun and Zhao, Xueping and He, Jing and Ding, Mingrui},journal={Cell Insight},volume={5},pages={100309},year={2026},doi={10.1016/j.cellin.2026.100309},}
Stud. Appl. Math.
Surface Wave Solutions in 1D and 2D for the Broer–Kaup–Boussinesq–Kupershmidt (BKBK) System
@article{holm2026surfacewavesbkbk,title={Surface Wave Solutions in 1D and 2D for the Broer--Kaup--Boussinesq--Kupershmidt (BKBK) System},author={Holm, Darryl D. and Hu, Ruiao and Wang, Hanchun},journal={Studies in Applied Mathematics},volume={156},pages={e70165},year={2026},doi={10.1111/sapm.70165},}
2025
arXiv
Compound Burgers-KdV Soliton Behaviour: Refraction, Reflection and Fusion
Darryl D. Holm, Ruiao Hu, Oliver D. Street, and 1 more author
@article{holm2025compoundburgerskdv,title={Compound Burgers-KdV Soliton Behaviour: Refraction, Reflection and Fusion},author={Holm, Darryl D. and Hu, Ruiao and Street, Oliver D. and Wang, Hanchun},journal={arXiv preprint arXiv:2505.17026},year={2025},archiveprefix={arXiv},primaryclass={nlin.PS},doi={10.48550/arXiv.2505.17026},url={https://arxiv.org/abs/2505.17026}}
SEAMS 2025
Robust Probabilistic Model Checking with Continuous Reward Domains
Xiaotong Ji, Hanchun Wang, Antonio Filieri, and 1 more author
In 2025 IEEE/ACM 20th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 2025
@inproceedings{Ji2025SEAMS,title={Robust Probabilistic Model Checking with Continuous Reward Domains},author={Ji, Xiaotong and Wang, Hanchun and Filieri, Antonio and Epifani, Ilenia},booktitle={2025 IEEE/ACM 20th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)},pages={13--24},year={2025},}
2024
STUOD 2023
Collisions of Burgers Bores with Nonlinear Waves
Albert. Dombret, Darryl D. Holm, Ruiao Hu, and 2 more authors
In Stochastic Transport in Upper Ocean Dynamics Annual Workshop, 2024
This chapter treats nonlinear wave-current interactions in their simplest form—as an overtaking collision. In one spatial dimension, the chapter investigates the collision interaction formulated as an initial value problem of a Burgers bore overtaking solutions of two types of nonlinear wave equations—Korteweg–de Vries (KdV) and nonlinear Schrödinger (NLS). The bore-wave state arising after the overtaking Burgers-KdV collision in numerical simulations is found to depend qualitatively on the balance between nonlinearity and dispersion in the KdV equation. The Burgers-KdV system is also made stochastic by following the stochastic advection by Lie transport approach (SALT).
@incollection{dombret2024collisionsburgersboresnonlinear,title={Collisions of Burgers Bores with Nonlinear Waves},author={Dombret, Albert. and Holm, Darryl D. and Hu, Ruiao and Street, Oliver D. and Wang, Hanchun},booktitle={Stochastic Transport in Upper Ocean Dynamics Annual Workshop},year={2024},pages={25--43},eprint={2405.08130},archiveprefix={arXiv},primaryclass={physics.flu-dyn},url={https://arxiv.org/abs/2405.08130},doi={10.1007/978-3-031-70660-8_2},}
MICCAI 2024
Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis
Liu Li, Hanchun Wang, Matthew Baugh, and 5 more authors
In Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, 2024
Although existing medical image segmentation methods provide impressive pixel-wise accuracy, they often neglect topological correctness, making their segmentations unusable for many downstream tasks. One option is to retrain such models whilst including a topology-driven loss component. However, this is computationally expensive and often impractical. A better solution would be to have a versatile plug-and-play topology refinement method that is compatible with any domain-specific segmentation pipeline. Directly training a post-processing model to mitigate topological errors often fails as such models tend to be biased towards the topological errors of a target segmentation network. The diversity of these errors is confined to the information provided by a labelled training set, which is especially problematic for small datasets. Our method solves this problem by training a model-agnostic topology refinement network with synthetic segmentations that cover a wide variety of topological errors. Inspired by the Stone-Weierstrass theorem, we synthesize topology-perturbation masks with randomly sampled coefficients of orthogonal polynomial bases, which ensures a complete and unbiased representation. Practically, we verified the efficiency and effectiveness of our methods as being compatible with multiple families of polynomial bases, and show evidence that our universal plug-and-play topology refinement network outperforms both existing topology-driven learning-based and post-processing methods. We also show that combining our method with learning-based models provides an effortless add-on, which can further improve the performance of existing approaches.
@inproceedings{10.1007/978-3-031-72114-4_64,author={Li, Liu and Wang, Hanchun and Baugh, Matthew and Ma, Qiang and Zhang, Weitong and Ouyang, Cheng and Rueckert, Daniel and Kainz, Bernhard},title={Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis},booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},year={2024},doi={10.1007/978-3-031-72114-4_64},publisher={Springer Nature Switzerland},pages={670--680},}
CAG
Executing realistic earthquake simulations in unreal engine with material calibration
Yitong Sun, Hanchun Wang, Zhejun Zhang, and 2 more authors
Earthquakes significantly impact societies and economies, underscoring the need for effective search and rescue strategies. As AI and robotics increasingly support these efforts, the demand for high-fidelity, real-time simulation environments for training has become pressing. Earthquake simulation can be considered as a complex system. Traditional simulation methods, which primarily focus on computing intricate factors for single buildings or simplified architectural agglomerations, often fall short in providing realistic visuals and real-time structural damage assessments for urban environments. To address this deficiency, we introduce a real-time, high visual fidelity earthquake simulation platform based on the Chaos Physics System in Unreal Engine, specifically designed to simulate the damage to urban buildings. Initially, we use a genetic algorithm to calibrate material simulation parameters from Ansys into the Unreal Engine’s fracture system, based on real-world test standards. This alignment ensures the similarity of results between the two systems while achieving real-time capabilities. Additionally, by integrating real earthquake waveform data, we improve the simulation’s authenticity, ensuring it accurately reflects historical events. All functionalities are integrated into a visual user interface, enabling zero-code operation, which facilitates testing and further development by cross-disciplinary users. We verify the platform’s effectiveness through three AI-based tasks: similarity detection, path planning, and image segmentation. This paper builds upon the preliminary earthquake simulation study we presented at IMET 2023, with significant enhancements, including improvements to the material calibration workflow and the method for binding building foundations.
2023
IEEE VR Workshops
Predicting the Light Spectrum of Virtual Reality Scenarios for Non-Image-Forming Visual Evaluation
Yitong Sun, Hanchun Wang, Pinar Satilmis, and 3 more authors
In 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2023
IMET 2023
RESenv: A Realistic Earthquake Simulation Environment based on Unreal Engine
Yitong Sun, Hanchun Wang, Zhejun Zhang, and 2 more authors
@article{sun2023resenvrealisticearthquakesimulation,title={RESenv: A Realistic Earthquake Simulation Environment based on Unreal Engine},author={Sun, Yitong and Wang, Hanchun and Zhang, Zhejun and Diels, Cyriel and Asadipour, Ali},journal={IMET 2023},year={2023},eprint={2311.07239},archiveprefix={arXiv},primaryclass={cs.CE},url={https://arxiv.org/abs/2311.07239},}
@article{Golden_Ratio,title={The Golden Ratio and Hydrodynamics},doi={10.1007/s00283-021-10099-1},journal={The Mathematical Intelligencer},author={Khesin, Boris and Wang, Hanchun},volume={44},number={1},year={2022},pages={22--27},}