Multimodal neural operators predict full-field brain displacement from MRE data with high accuracy and fast inference by fusing volumetric imaging, demographics, and acquisition parameters.
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Numerical study comparing feedforward NN and DeepONet with data-driven and physics-informed losses on stochastic heat equation, highlighting larger errors at distribution tails due to extrapolation.
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Multimodal Neural Operators for Real-Time Biomechanical Modelling of Traumatic Brain Injury
Multimodal neural operators predict full-field brain displacement from MRE data with high accuracy and fast inference by fusing volumetric imaging, demographics, and acquisition parameters.
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A numerical study into neural network surrogate model performance for uncertainty propagation
Numerical study comparing feedforward NN and DeepONet with data-driven and physics-informed losses on stochastic heat equation, highlighting larger errors at distribution tails due to extrapolation.