Inducing Permutation Invariant Priors in Bayesian Optimization for Carbon Capture and Storage Applications
arXiv:2605.02409v2 Announce Type: replace Abstract: Bayesian Optimization is an iterative method, tailored to optimizing expensive black box objective functions. Surrogate models like Gaussian Processes, which are the gold standard in Bayesian Optimization, can be inefficient for inputs with permutation symmetries, as the most common kernels employed are better suited for vector inputs rather than unordered sets of items. Motivated by this issue, we turn to permutation invariant Bayesian Optimization for well placement in Carbon Capture and Storage projects. The high fidelity black box simulat
The increasing focus on sustainable energy solutions like Carbon Capture and Storage necessitates more efficient optimization methods, aligning with advancements in AI to tackle complex engineering problems.
Improving Bayesian Optimization for applications like Carbon Capture and Storage demonstrates AI's potential to accelerate the development and deployment of critical climate technologies, reducing costs and increasing efficiency.
The adaptation of AI optimization techniques to handle permutation symmetries in data improves the feasibility and performance of complex black-box simulations, particularly for resource-intensive projects.
- · Carbon Capture and Storage sector
- · AI/ML researchers
- · Environmental engineering firms
- · Traditional simulation optimization methods
- · High-emissions industries (indirectly, as CCS becomes more viable)
More efficient and cost-effective design and operation of Carbon Capture and Storage facilities.
Accelerated deployment and adoption of CCS technologies globally due to improved economic viability.
Enhanced efforts to meet climate targets, with AI playing a foundational role in decarbonization strategies.
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Read at arXiv cs.LG