Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems
arXiv:2602.11208v2 Announce Type: replace Abstract: The Earth's subsurface is a cornerstone of modern society, providing essential energy resources like hydrocarbons, geothermal, and minerals while serving as the primary reservoir for $CO_2$ sequestration. However, full physics numerical simulations of these systems are notoriously computationally expensive due to geological heterogeneity, high resolution requirements, and the tight coupling of physical processes with distinct propagation time scales. Here we propose the $\textbf{Adaptive Physics Transformer}$ (APT), a geometry-, mesh-, and ph
Advances in AI, particularly transformer architectures, are increasingly being applied to complex scientific and engineering challenges now that computational resources and algorithmic sophistication have reached a critical threshold.
Improving the efficiency of subsurface energy system simulations through AI can significantly reduce the cost and time required for resource extraction, carbon sequestration, and geothermal development, impacting global energy security and climate goals.
Traditional computationally expensive physics-based simulations for subsurface systems can be dramatically accelerated and made more accessible through advanced AI models, potentially leading to faster and more accurate resource management.
- · Energy companies (geothermal, O&G)
- · AI/ML model developers
- · Environmental engineering firms
- · Geoscience researchers
- · Traditional simulation software vendors (if they don't adapt)
- · High-latency computational methods
More efficient and cost-effective exploration and management of subsurface energy resources.
Accelerated development of geothermal energy and carbon capture technologies due to reduced simulation bottlenecks.
Potential for new energy resource discoveries previously deemed too complex or costly to model and extract, altering geopolitical energy landscapes.
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