Nonlocal kernel network (NKN): A stable and resolution-independent deep neural network
Abstract Neural operators [1],[2],[3],[4],[5] have recently become popular tools for designing
solution maps between function spaces in the form of neural networks. Differently from …
solution maps between function spaces in the form of neural networks. Differently from …
Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks
We propose using operator learning to approximate the dynamical response of non-
autonomous systems, such as nonlinear control systems. Unlike classical function learning …
autonomous systems, such as nonlinear control systems. Unlike classical function learning …
[HTML][HTML] Multifidelity deep operator networks for data-driven and physics-informed problems
Operator learning for complex nonlinear systems is increasingly common in modeling multi-
physics and multi-scale systems. However, training such high-dimensional operators …
physics and multi-scale systems. However, training such high-dimensional operators …
Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network …
Modeling and prediction of the dynamic behavior of thermal systems operating under
intermittent energy input and variable load requirements represent one of the greatest …
intermittent energy input and variable load requirements represent one of the greatest …
Deeponet-grid-uq: A trustworthy deep operator framework for predicting the power grid's post-fault trajectories
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 …
grid's post-fault trajectories, ie, the power grid's dynamic response after a disturbance or …
[PDF][PDF] Multifidelity deep operator networks
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) …
Howard Mauro Perego, George Karniadakis, Panos Stinis Page 2 2 General framework u(x1) …
A hybrid deep neural operator/finite element method for ice-sheet modeling
One of the most challenging and consequential problems in climate modeling is to provide
probabilistic projections of sea level rise. A large part of the uncertainty of sea level …
probabilistic projections of sea level rise. A large part of the uncertainty of sea level …
Bi-fidelity modeling of uncertain and partially unknown systems using deeponets
Recent advances in modeling large-scale complex physical systems have shifted research
focuses towards data-driven techniques. However, generating datasets by simulating …
focuses towards data-driven techniques. However, generating datasets by simulating …
A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures
This work is directed to uncertainty quantification of homogenized effective properties for
composite materials with complex, three dimensional microstructure. The uncertainties arise …
composite materials with complex, three dimensional microstructure. The uncertainties arise …
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows
This work proposes a new machine learning (ML)-based paradigm aiming to enhance the
computational efficiency of non-equilibrium reacting flow simulations while ensuring …
computational efficiency of non-equilibrium reacting flow simulations while ensuring …