Machine learning for membrane bioreactor research: principles, methods, applications, and a tutorial

Y Lai, K Xiao, Y He, X Liu, J Tan, W Xue… - … Science & Engineering, 2025 - Springer
Membrane fouling poses a significant challenge to the sustainable development of
membrane bioreactor (MBR) technologies for wastewater treatment. The accurate prediction …
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[PDF] ssrn.com

An enhanced hybrid adaptive physics-informed neural network for forward and inverse PDE problems

K Luo, S Liao, Z Guan, B Liu - Applied Intelligence, 2025 - Springer
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving
partial differential equations (PDEs) in various scientific and engineering applications …
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[PDF] arxiv.org

Parametric Taylor series based latent dynamics identification neural networks

X Lin, D Xiao - arXiv preprint arXiv:2410.04193, 2024 - arxiv.org
Numerical solving parameterised partial differential equations (P-PDEs) is highly practical
yet computationally expensive, driving the development of reduced-order models (ROMs) …
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Physical informed memory networks based on domain decomposition for solving nonlinear partial differential equations

J Sun, H Dong, M Liu, Y Fang - The European Physical Journal Special …, 2024 - Springer
In recent years, deep learning models have emerged as a popular numerical method for
solving nonlinear partial differential equations (PDEs). In this paper, the improved physical …
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[PDF] arxiv.org

KH-PINN: Physics-informed neural networks for Kelvin-Helmholtz instability with spatiotemporal and magnitude multiscale

J Wu, Y Wu, X Li, G Zhang - arXiv preprint arXiv:2411.07524, 2024 - arxiv.org
Prediction of Kelvin-Helmholtz instability (KHI) is crucial across various fields, requiring
extensive high-fidelity data. However, experimental data are often sparse and noisy, while …
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