Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential …
Deep neural operators can learn operators mapping between infinite-dimensional function spaces via deep neural networks and have become an emerging paradigm of scientific …
Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and …
This article provides an overview of multi-fidelity modeling trends. Fidelity in modeling refers to the level of detail and accuracy provided by a predictive model or simulation. Generally …
Deep neural operators, such as DeepONet, have changed the paradigm in high- dimensional nonlinear regression, paving the way for significant generalization and speed …
Operator learning for complex nonlinear systems is increasingly common in modeling multi- physics and multi-scale systems. However, training such high-dimensional operators …
This paper proposes a novel data-driven method for the reliable prediction of the power grid's post-fault trajectories, ie, the power grid's dynamic response after a disturbance or …
S Venturi, T Casey - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
Deep operator networks (DeepONets) are powerful and flexible architectures that are attracting attention in multiple fields due to their utility for fast and accurate emulation of …
AA Howard, M Perego… - arXiv preprint arXiv …, 2022 - app.icerm.brown.edu
Multifidelity Deep Operator Networks Page 1 Multifidelity Deep Operator Networks Amanda Howard Mauro Perego, George Karniadakis, Panos Stinis Page 2 2 General framework u(x1) …