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- W4387144502 abstract "Abstract Discovering physics from data have the potential to advance our understanding and prediction of a system where the governing physics are unknown but experimental data are available. Physics-based models are conventionally obtained from rigorous first principles derivations such as conservation laws or knowledge-based phenomenological derivations. However, inferring physics-based models remains challenging for many real-world complex systems with intertwined and coupled multi-physics that are challenging to derive from first principles. In recent years, data-driven physics discovery has become an attractive research topic across many scientific disciplines due to the increasing richness of observational data and advances in machine learning. Various data-driven physics discovery algorithms have been proposed to extract simple but appropriate models governing the systems from the observed data. Among the proposed algorithms, genetic programming (GP) based symbolic regression (SR) plays a dominant role. Here, we build a general GP based SR platform to discover physical models from observations for real-world systems. The platform is used to solve an optimization problem where the outcome is a set of human-readable equations that the user can select from based on accuracy vs parsimony trades. The discovery process also considers the prior knowledge of experts, which manifests as constraints to enhance the embedding of physics and avoid overfitting. The discovered models not only have high interpretability but also demonstrate good extrapolation performance." @default.
- W4387144502 created "2023-09-29" @default.
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- W4387144502 date "2023-06-26" @default.
- W4387144502 modified "2023-10-18" @default.
- W4387144502 title "Physics Discovery of Engineering Applications With Constrained Optimization and Genetic Programming" @default.
- W4387144502 doi "https://doi.org/10.1115/gt2023-104043" @default.
- W4387144502 hasPublicationYear "2023" @default.
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