STONet, a neural operator combining DeepONet with transformer-style attention, predicts contaminant concentration fields in micro-cracked reservoirs with reported relative errors below 1% against finite-element simulations and about 100x speedup.
We compare the accuracy and computational efficiency of our approach to the227 finite element method
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STONet: A neural operator for modeling solute transport in micro-cracked reservoirs
STONet, a neural operator combining DeepONet with transformer-style attention, predicts contaminant concentration fields in micro-cracked reservoirs with reported relative errors below 1% against finite-element simulations and about 100x speedup.