Stacking ML & AI surrogate models based on portfolio optimization strategies

What is it about?

This paper introduces a new method for improving the performance of AI and ML surrogate models. Surrogate models are like stand-ins for expensive simulations, and typically, one is chosen based on past experience or by comparing their errors. This paper presents a fresh approach by looking at the problem as a financial one, similar to managing an investment portfolio. In simple terms, the authors treat the errors made by different surrogate models as random variables. We use a clustering algorithm to select a subset of the best-performing models and calculate their weights, similar to how you would manage assets in a financial portfolio. This new approach significantly enhances the overall performance of the ensemble of models. To test our approach, we trained a large number of surrogate models and found that the clustering algorithm was able to select a smaller group of high-performing models, reducing the complexity while preserving diversity. Moreover, we demonstrated that the portfolio management approach led to better results for various input scenarios. In essence, this paper presents a way to improve the accuracy of surrogate models by borrowing ideas from finance. We show that this novel approach can be a valuable tool for engineers and researchers working with complex simulations.

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Photo by Brett Jordan on Unsplash

Photo by Brett Jordan on Unsplash

Why is it important?

The work is important as it introduces an innovative approach to enhance the efficiency of ML & AI predictive tasks through surrogate models. By likening model selection to financial portfolio management, it offers a systematic, data-driven method for choosing the best models, reducing computational costs while maintaining diversity. This cross-disciplinary perspective not only speeds up the design and optimization processes but also has broader implications for problem-solving in various domains. The practical case study demonstrates its real-world applicability, making it a valuable tool for researchers and engineers.