Amanda A. Howard

Orcid: 0000-0002-6411-6198

According to our database1, Amanda A. Howard authored at least 13 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2024
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks.
CoRR, 2024

Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems.
CoRR, 2024

Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems.
CoRR, 2024

2023
Multifidelity deep operator networks for data-driven and physics-informed problems.
J. Comput. Phys., November, 2023

A hybrid deep neural operator/finite element method for ice-sheet modeling.
J. Comput. Phys., November, 2023

Stacked networks improve physics-informed training: applications to neural networks and deep operator networks.
CoRR, 2023

A multifidelity approach to continual learning for physical systems.
CoRR, 2023

2022
Multifidelity Deep Operator Networks.
CoRR, 2022

Machine Learning in Heterogeneous Porous Materials.
CoRR, 2022

2021
A conservative level set method for <i>N</i>-phase flows with a free-energy-based surface tension model.
J. Comput. Phys., 2021

Physics-informed CoKriging model of a redox flow battery.
CoRR, 2021

2020
Non-local model for surface tension in fluid-fluid simulations.
J. Comput. Phys., 2020

Learning Unknown Physics of non-Newtonian Fluids.
CoRR, 2020


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