Towards Solving Industry-Grade Surrogate Modeling Problems using Physics Informed Machine Learning
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Deep learning combined with physics-based modeling represents an attractive and efficient approach for producing accurate and robust surrogate modeling. In this paper, a new framework that utilizes Physics Informed Neural Networks (PINN) to solve PDE-based problems for the creation of surrogate models for steady-state flow-thermal engineering design applications is introduced. The surrogate models developed through this framework are demonstrated on several use cases from electronics cooling to biomechanics. Additionally, it is demonstrated how these trained surrogate models can be combined with design optimization methods to improve the efficiency and reduced the cost of the design process. The former is shown through several realistic 3D examples and the latter via a detailed cost-benefit trade off. Overall, the findings of this paper demonstrate that hybrid data-PINN surrogate models combined with optimization algorithms can solve realistic design optimization and have potential in a wide variety of application areas.
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