<ul data-eligibleForWebStory="true"><li>Researchers introduce a new neural operator, STONet, to model contaminant transport in micro-cracked porous media efficiently.</li><li>STONet architecture includes a DeepONet structure enriched with a transformer-based multi-head attention mechanism, enhancing performance without added computational overhead.</li><li>The model integrates different networks to encode properties effectively, predict concentration field changes accurately, and achieves relative errors below 1% compared to FEM simulations.</li><li>STONet's computational efficiency allows for rapid assessment of subsurface contamination risks and optimization of environmental remediation strategies.</li></ul>

STONet: A neural operator for modeling solute transport in micro-cracked reservoirs

Discover more