arXiv:2606.24967v1 Announce Type: new Abstract: In ill-posed inverse problems, the recovered solution depends as much on the prior as on the data, yet much of the engineering knowledge that could serve as that prior is recorded qualitatively rather than in formal mathematical form. Here we test whether sentence embeddings can act as an inference-time interface for injecting geological descriptions into a learned Darcy-flow inverse solver. Across six synthetic geological classes and an exploratory transfer to a benchmark reservoir model (SPE10), we vary only the conditioning representation and
The paper investigates the current challenge of integrating qualitative engineering knowledge into quantitative inverse problems, leveraging advancements in natural language processing and AI.
This research suggests a novel method for AI systems to interpret and apply qualitative human knowledge, moving towards more intelligent and adaptable problem-solving in complex domains like geology.
The ability to use sentence embeddings as an inference-time interface for geological descriptions changes how prior knowledge can be injected into learned inverse solvers.
- · Geological exploration companies
- · AI/ML researchers in inverse problems
- · Petroleum engineering sector
- · Environmental monitoring
Improved accuracy and efficiency in solving complex inverse problems by integrating qualitative human expertise.
Development of hybrid AI systems that seamlessly blend symbolic reasoning with sub-symbolic learning using natural language inputs.
Enhanced automation and interpretability of scientific discovery processes across various domains that rely on inverse modeling.
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Read at arXiv cs.LG