5 papers
Gaussian process learning with flow map refinement for parameter estimation in dynamical systems
Yue Hao, Dongwei Ye
Parameter estimation is a central task in data-driven learning of dynamical systems. It aims to recover the underlying physical parameters from observed time-series data, thereby p…
Radially symmetric transition-layer solutions in mass-conserving reaction-diffusion systems with bistable nonlinearity
Xiaoqing He, Quan-Xing Liu, Dong Ye
Mass-conserving reaction-diffusion (MCRD) systems are widely used to model phase separation and pattern formation in cell polarity, biomolecular condensates, and ecological systems…
RONOM: Reduced-Order Neural Operator Modeling
Sven Dummer, Dongwei Ye, Christoph Brune
Time-dependent partial differential equations are ubiquitous in physics-based modeling, but they remain computationally intensive in many-query scenarios, such as real-time forecas…
TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians
Letian Huang, Dongwei Ye, Jialin Dan +7
The emergence of neural and Gaussian-based radiance field methods has led to considerable advancements in novel view synthesis and 3D object reconstruction. Nonetheless, specular r…
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Marc Fransen, Andreas Fürst, Deepak Tunuguntla +21
Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights…