Wuyue Yang

Incorporating continuous dependence qualifies physics-informed neural networks for operator learning

Guojie Li, Wuyue Yang, Liu Hong

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A Perspective on Multiscale Electrodiffusion and Model Selection for Ion Transport in Energy-Storage Nanomaterials

Hamid Mofidi, Wuyue Yang, Mingji Zhang

Ion transport in energy storage nanomaterials is described with models that resolve different scales and different physical effects. Device models such as the Doyle-Fuller-Newman framework are well suited to cell voltage, concentration polarization, and electrode utilization, but they usually average local charge …

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Data-Driven Phase-Aware Knowledge Distillation for Stiff Chemical Reaction Networks

Wuyue Yang, Sheng Ran, Liu Hong

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Exploring Multiple Timescale Dynamics Using Geometric Singular Perturbation-Informed Neural Networks (GSPINNs)

Hamid Mofidi, Maziar Raissi, Wuyue Yang

Abstract. Multiple timescale systems have long been a subject of extensive study, with geometric singular perturbation theory (GSPT) emerging as a common tool for analyzing such systems. In this work, we present a comprehensive study of ordinary differential equations in the form …

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STWCR: Weak collocation regression for revealing hidden stochastic dynamics from single trajectory data

Z. Zeng, Wuyue Yang, Hong Liu, Pipi Hu et autres

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Improving generalization ability of deep-learning-based ODE solvers using continuous dependence

Guojie Li, Sheng Ran, Wuyue Yang, Liu Hong

Inspired by the well-known mathematical statements on the continuous dependence of solutions to ordinary differential equations on initial values and parameters, we make a non-trivial extension of the physics-informed neural networks by incorporating additional information on the continuous dependence of solutions (abbreviated …

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MEP-Net: Generating solutions to scientific problems with limited knowledge by maximum entropy principle

Wuyue Yang, Liangrong Peng, Guojie Li, Liu Hong

Maximum entropy principle (MEP) offers an effective and unbiased approach to inferring unknown probability distributions when faced with incomplete information, while neural networks provide the flexibility to learn complex distributions from data. This paper proposes a novel neural network architecture, the MEP-Net, …

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Tailored knowledge distillation with automated loss function learning

Sheng Ran, Tao Huang, Wuyue Yang

Knowledge Distillation (KD) is one of the most effective and widely used methods for model compression of large models. It has achieved significant success with the meticulous development of distillation losses. However, most state-of-the-art KD losses are manually crafted and task-specific, raising …

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Thermodynamics for reduced models of polymer aggregation based on maximum entropy principle

Xinyu Zhang, Wuyue Yang, Liangrong Peng, Liu Hong

Polymeric aggregates play a significant role in biology and chemical engineering. In order to make a clear description of their underlying formation procedure, simplified models are crucial because the original mass-action equations involve numerous variables, complicating analysis and understanding. While the dynamical …

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Extracting interaction kernels for many-particle systems by a two-phase approach

Y. Shi, Wuyue Yang, Liu Hong

This paper presents a two-phase method for learning interaction kernels of stochastic many-particle systems. After transforming stochastic trajectories of every particle into the particle density function by the kernel density estimation method, the first phase of our approach combines importance sampling with …

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Stwcr: Weak Collocation Regression for Revealing Hidden Stochastic Dynamics from Single Trajectory Data

Yan Jiang, Zhijun Zeng, Wuyue Yang, Hong Liu et autres

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Extracting Interaction Kernels for Many-Particle Systems by a Two-Phase Approach

Wuyue Yang, Hong Shi Liu

This paper presents a two-phase method for learning interaction kernels of stochastic many-particle systems. After transforming stochastic trajectories of every particle into the particle density function by the kernel density estimation method, the first phase of our approach combines importance sampling with …