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[HTML][HTML] Physics-aware recurrent convolutional neural networks for modeling multiphase compressible flows
Multiphase compressible flow systems can exhibit unsteady and fast-transient dynamics,
marked by sharp gradients and discontinuities, and material boundaries that interact with the …
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
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 …
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
Despite substantial advances in numerical simulation techniques, constructing a real-time
optimization framework with accurate and fast predictions remains challenging. The difficulty …
optimization framework with accurate and fast predictions remains challenging. The difficulty …
Sub-Sequential Physics-Informed Learning with State Space Model
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 …
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
Many applications in computational and experimental fluid mechanics require effective
methods for reconstructing the flow fields from limited sensor data. However, this task …
methods for reconstructing the flow fields from limited sensor data. However, this task …