[HTML][HTML] Physics-aware recurrent convolutional neural networks for modeling multiphase compressible flows

X Cheng, PCH Nguyen, PK Seshadri, M Verma… - International Journal of …, 2024 - Elsevier
Multiphase compressible flow systems can exhibit unsteady and fast-transient dynamics,
marked by sharp gradients and discontinuities, and material boundaries that interact with the …

[HTML][HTML] SAG's Overload Forecasting Using a CNN Physical Informed Approach

R Hermosilla, C Valle, H Allende, C Aguilar, E Lucic - Applied Sciences, 2024 - mdpi.com
The overload problem in semi-autogenous grinding (SAG) mills is critical in the mining
industry, impacting the extraction of valuable metals and overall productivity. Overloads can …

Physics-aware machine learning for computational fluid dynamics surrogate model to estimate ventilation performance

M Kim, NK Chau, S Park, PCH Nguyen, SS Baek… - Physics of …, 2025 - pubs.aip.org
Despite substantial advances in numerical simulation techniques, constructing a real-time
optimization framework with accurate and fast predictions remains challenging. The difficulty …

Sub-Sequential Physics-Informed Learning with State Space Model

C Xu, D Liu, Y Hu, J Li, R Qin, Q Zheng… - arxiv preprint arxiv …, 2025 - arxiv.org
Physics-Informed Neural Networks (PINNs) are a kind of deep-learning-based numerical
solvers for partial differential equations (PDEs). Existing PINNs often suffer from failure …

FLRNet: A Deep Learning Method for Regressive Reconstruction of Flow Field From Limited Sensor Measurements

PCH Nguyen, JB Choi, QT Luu - arxiv preprint arxiv:2411.13815, 2024 - arxiv.org
Many applications in computational and experimental fluid mechanics require effective
methods for reconstructing the flow fields from limited sensor data. However, this task …