Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

MIONet: Learning Multiple-Input Operators via Tensor Product

Journal Article · · SIAM Journal on Scientific Computing
DOI:https://doi.org/10.1137/22m1477751· OSTI ID:1980779

Not provided.

Research Organization:
Univ. of Pennsylvania, Philadelphia, PA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0022953
OSTI ID:
1980779
Journal Information:
SIAM Journal on Scientific Computing, Vol. 44, Issue 6; ISSN 1064-8275
Publisher:
Society for Industrial and Applied Mathematics (SIAM)
Country of Publication:
United States
Language:
English

References (24)

DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks journal July 2021
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems journal July 1995
The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems journal February 2018
A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials journal March 2022
Physics-informed machine learning journal May 2021
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks journal January 2020
Operator learning for predicting multiscale bubble growth dynamics journal March 2021
A seamless multiscale operator neural network for inferring bubble dynamics journal October 2021
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators journal March 2021
DeepXDE: A Deep Learning Library for Solving Differential Equations journal January 2021
Physics-Informed Neural Networks with Hard Constraints for Inverse Design journal January 2021
DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators journal December 2021
The Random Feature Model for Input-Output Maps between Banach Spaces journal January 2021
Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network architecture journal January 2022
fPINNs: Fractional Physics-Informed Neural Networks journal January 2019
A physics-informed operator regression framework for extracting data-driven continuum models journal January 2021
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations journal February 2019
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations journal January 2020
DGM: A deep learning algorithm for solving partial differential equations journal December 2018
GMLS-Nets: A Framework for Learning from Unstructured Data report September 2019
Learning the solution operator of parametric partial differential equations with physics-informed DeepONets journal October 2021
Systems biology informed deep learning for inferring parameters and hidden dynamics journal November 2020
Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator–regression neural network journal February 2022
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems journal November 2019

Similar Records

Related Subjects