| RESEARCH CONNECTIONS |
Data-driven surrogates, physics-informed machine learning, and physics discovery represent three key approaches in the application of machine learning to physical systems. Data-driven surrogates rely heavily on large amounts of data to approximate complex physical processes, offering ease of training and flexibility across applications. However, these models often fail to honor the underlying physics, leading to potential inaccuracies outside of their training domain and dependence on abundant, high-quality data. Physics-informed ML directly integrates governing equations or physical laws into the learning process, allowing the model to generalize better with limited data. This approach enhances model interpretability and reduces reliance on data, but it can suffer from training instability due to challenges like enforcing boundary conditions or balancing loss terms. For instance, incorporating conservation laws in fluid dynamics modeling often leads to slow convergence or difficulty handling stiff systems. Lastly, physics discovery aims to uncover governing equations from data, leveraging explainable AI techniques to identify the underlying physical laws governing a system. This approach ensures alignment with physics and provides interpretable, symbolic representations of the discovered equations, making it particularly useful in fields where the governing equations are not well known or need refinement. This talk will explore these three approaches in detail, examining their underlying mechanisms, trade-offs, and relationships, providing insights into how they complement one another in solving complex physical problems. |
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