Presentation Information
[430101-01-01]Physics-informed Machine Learning: a powerful computer modelling framework for engineering and science
Prof. YuanTong Gu (Queensland University of Technology)
In recent years, physics-informed neural networks (PINNs) have revolutionized the application of machine learning to solving partial differential equations (PDEs). By combining data-driven and physics-based models, PINNs retain the strengths of both approaches, showing exceptional potential in addressing a wide range of complex problems. As a result, they have garnered increasing attention in many applications, particularly for problems with strong nonlinearities. The PINN has been becoming a game-changer for computer modelling and simulation for engineering and science.
This talk will first review the latest advancements in the use of PINNs for mechanics, including solid mechanics, nonlinear mechanics, fracture analysis, structural optimization, fluid mechanics, and more. Next, the challenges in applying PINNs to mechanics will be discussed. Finally, recent research from the speaker’s group will be presented, covering topics such as the new neural network architecture in PINN for mechanics, PINN-based structural topology optimization, food drying modelling, dynamic and nonlinear problem solving, and inverse problems. It has proven that physics-informed machine learning will be the new generation of a computer modelling framework for mechanics.
This talk will first review the latest advancements in the use of PINNs for mechanics, including solid mechanics, nonlinear mechanics, fracture analysis, structural optimization, fluid mechanics, and more. Next, the challenges in applying PINNs to mechanics will be discussed. Finally, recent research from the speaker’s group will be presented, covering topics such as the new neural network architecture in PINN for mechanics, PINN-based structural topology optimization, food drying modelling, dynamic and nonlinear problem solving, and inverse problems. It has proven that physics-informed machine learning will be the new generation of a computer modelling framework for mechanics.