Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making
📰 ArXiv cs.AI
Learn how to apply interpretable and explainable surrogate modeling for simulations to improve decision-making with Explainable AI
Action Steps
- Apply surrogate modeling techniques to reduce computational costs in simulations
- Use explainable AI methods to interpret surrogate models and understand input variable effects
- Evaluate the performance of surrogate models using metrics such as accuracy and fidelity
- Implement techniques to improve model transparency, such as feature attribution and model interpretability
- Integrate explainable AI into the simulation workflow to support decision-making
Who Needs to Know This
Data scientists and researchers working on complex system simulations can benefit from this survey to improve model interpretability and explainability, enabling better decision-making
Key Insight
💡 Explainable AI can enhance the interpretability and transparency of surrogate models, leading to better decision-making in complex system simulations
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🤖 Improve simulation-based decision-making with interpretable & explainable surrogate modeling! 💡
Key Takeaways
Learn how to apply interpretable and explainable surrogate modeling for simulations to improve decision-making with Explainable AI
Full Article
Title: Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making
Abstract:
arXiv:2604.14240v1 Announce Type: new Abstract: The simulation of complex systems increasingly relies on sophisticated but fundamentally opaque computational black-box simulators. Surrogate models play a central role in reducing the computational cost of complex systems simulations across a wide range of scientific and engineering domains. Notwithstanding, they inevitably inherit and often exacerbate this black-box nature, obscuring how input variables drive physical responses. Conversely, Expla
Abstract:
arXiv:2604.14240v1 Announce Type: new Abstract: The simulation of complex systems increasingly relies on sophisticated but fundamentally opaque computational black-box simulators. Surrogate models play a central role in reducing the computational cost of complex systems simulations across a wide range of scientific and engineering domains. Notwithstanding, they inevitably inherit and often exacerbate this black-box nature, obscuring how input variables drive physical responses. Conversely, Expla
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