Physics-informed deep learning for data-driven solutions of computational fluid dynamics

Engineering

• Computational Fluid Dynamics 100%
• Deep Learning Method 100%
• Surrogate Model 50%
• Similarities 25%
• Continuous Stirred Tank Reactor 25%
• Computational Time 25%
• Reactor System 25%
• Real-Time Optimisation 25%
• Process Chemical 25%
• Process Control 25%
• Chemical Reactor 25%
• Loss Function 25%
• Engineering Problem 25%
• Physical Meaning 25%
• Reference Frame 25%
• System State 25%
• Neural Network Architecture 25%

Chemical Engineering

• Deep Learning Method 100%
• Neural Network 100%
• Chemical Reactor 20%
• Process Control 20%
• Continuous Stirred Tank Reactor 20%