Lu Lu

TL;DR: A universal approximation theorem of continuous multiple-input operators is proved and a novel neural operator, MIONet, is proposed, which can learn solution operators involving systems governed by ordinary and partial differential equations.

TL;DR: DeepM&Mnet as discussed by the authors employs a special neural network for approximating nonlinear operators, the DeepONet, which is used to predict separately each individual field, given inputs from the rest of the fields of the coupled multiphysics system.

TL;DR: A particle model-resembling a coarse-grained molecular model-constructed to match the intermolecular contacts between HbS molecules is developed and it is demonstrated that the particle model predicts the formation of HBS polymer fibers by attachment of monomers to rough fiber ends and the growth rate increases linearly withHbS concentration.

TL;DR: This work demonstrates this collapse of deep and narrow NNs both numerically and theoretically, and provides estimates of the probability of collapse, and constructs a diagram of a safe region for designing NNs that avoid the collapse to erroneous states.

TL;DR: In this article, the authors developed deep learning methods to predict patient-specific blood glucose during various time horizons in the immediate future using patientspecific every 30-min long glucose measurements by the continuous glucose monitoring (CGM) to predict future glucose levels in 5min to 1h.