4 citations · 4 across the 5 of their papers we have counts for
7 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…
A parametric framework for kernel-based dynamic mode decomposition using deep learning
Konstantinos Kevopoulos, Dongwei Ye
Surrogate modelling is widely applied in computational science and engineering to mitigate computational efficiency issues for the real-time simulations of complex and large-scale…
Gaussian process learning of nonlinear dynamics
Dongwei Ye, Mengwu Guo
One of the pivotal tasks in scientific machine learning is to represent underlying dynamical systems from time series data. Many methods for such dynamics learning explicitly requi…