Stacked networks improve physics-informed training: applications to neural networks and deep operator networks
Physics-informed neural networks and operator networks have shown promise for effectively
solving equations modeling physical systems. However, these networks can be difficult or …
solving equations modeling physical systems. However, these networks can be difficult or …
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks
Physics-informed deep learning has emerged as a promising alternative for solving partial
differential equations. However, for complex problems, training these networks can still be …
differential equations. However, for complex problems, training these networks can still be …
Bi-fidelity variational auto-encoder for uncertainty quantification
Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary
objective in model validation. However, achieving this goal entails balancing the need for …
objective in model validation. However, achieving this goal entails balancing the need for …
Multifidelity kolmogorov-arnold networks
We develop a method for multifidelity Kolmogorov-Arnold networks (KANs), which use a low-
fidelity model along with a small amount of high-fidelity data to train a model for the high …
fidelity model along with a small amount of high-fidelity data to train a model for the high …
A Multifidelity Machine Learning Based Semi-Lagrangian Finite Volume Scheme for Linear Transport Equations and the Nonlinear Vlasov–Poisson System
Machine-learning (ML) based discretization has been developed to simulate complex partial
differential equations (PDEs) with tremendous success across various fields. These learned …
differential equations (PDEs) with tremendous success across various fields. These learned …
[HTML][HTML] Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks
W Lin, M Zou, M Zhao, J Chang, X Xie - Applied Sciences, 2024 - mdpi.com
The thermal insulation integrity of liquefied natural gas storage tanks is essential for their life-
cycle safety. However, perlite settlement (insulation material) can result in thermal leaks and …
cycle safety. However, perlite settlement (insulation material) can result in thermal leaks and …
Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling
AM Propp, DM Tartakovsky - arXiv preprint arXiv:2410.12241, 2024 - dl.begellhouse.com
The development of efficient surrogates of partial differential equations (PDEs) is a critical
step towards scalable modeling of complex, multiscale systems-of-systems. Convolutional …
step towards scalable modeling of complex, multiscale systems-of-systems. Convolutional …
[PDF][PDF] Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
N Chenga, OA Malikb, S Beckera… - arXiv preprint arXiv …, 2023 - researchgate.net
Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary
objective in model validation. However, achieving this goal entails balancing the need for …
objective in model validation. However, achieving this goal entails balancing the need for …