REVIEW 2 cited by
Improving physics-informed DeepONets with hard constraints
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Current physics-informed (standard or deep operator) neural networks still rely on accurately learning the initial and/or boundary conditions of the system of differential equations they are solving. In contrast, standard numerical methods involve such conditions in computations without needing to learn them. In this study, we propose to improve current physics-informed deep learning strategies such that initial and/or boundary conditions do not need to be learned and are represented exactly in the predicted solution. Moreover, this method guarantees that when a deep operator network is applied multiple times to time-step a solution of an initial value problem, the resulting function is at least continuous.
Forward citations
Cited by 2 Pith papers
-
Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators
NOEM replaces large FEM meshes with pretrained neural-operator elements inside a variational energy-minimization framework, cutting computation time.
-
Low-rank adaptive physics-informed HyperDeepONets for solving differential equations
A LoRA-style low-rank factorization of the hypernetwork output weight matrix reduces parameters in physics-informed HyperDeepONets and achieves equal or better accuracy on five ODE/PDE benchmarks.
Discussion (0). Sign in to comment.