EXPLORING ROBUSTNESS AND GENERALIZATION IN DATA SCIENCE MODELS THROUGH MULTI-FIDELITY SIMULATIONS AND TRANSFER LEARNING
Keywords:
Robustness, Generalization, Multi-Fidelity Simulations, Transfer Learning, Data Science Models, Machine Learning, Computational Efficiency, Knowledge TransferAbstract
The increasing complexity and volume of data in modern science and engineering have made it essential to develop models that generalize well across diverse contexts while maintaining robustness. This paper explores the integration of multi-fidelity simulations and transfer learning techniques to improve both robustness and generalization in data science models. By leveraging multi-fidelity simulations, we can generate multiple levels of data accuracy and computational costs, allowing for more efficient learning processes. Transfer learning further enhances model performance by transferring knowledge from pre-trained models to related tasks, reducing the need for extensive labeled data.
In this study, we evaluate the effectiveness of multi-fidelity simulations and transfer learning through a set of experiments on standard datasets, focusing on how these methods impact model robustness and generalization. Results indicate that combining these approaches significantly improves the ability of models to generalize across unseen data and maintain performance under various conditions. This research highlights the value of integrating advanced simulation techniques with machine learning models for achieving higher efficiency and adaptability in real-world applications.
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