DeepONet and Operator Learning
Published:
Conventional trials of physics-informed deep learning (say, PINNs) integrates the dynamics as constraints to guide the approximation of a certain function within. An obvious problem with this idea is that such models cannot be directly applied to different initial and boundary conditions and observations. DeepONet, representing a new trial in physics-informed learning, employed some tricks to get around with this. Read more