Amanda A. Howard

According to our database, Amanda A. Howard authored at least 23 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

SINDy-KANs: Sparse identification of non-linear dynamics through Kolmogorov-Arnold networks.

Amanda A. Howard, Nicholas Zolman, Bruno Jacob, Steven L. Brunton, Panos Stinis

CoRR, March, 2026

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications.

Emily Williams, Amanda A. Howard, Saad Qadeer, Brek Meuris, Panos Stinis

J. Comput. Phys., 2026

2025

Bridging quantum and classical computing for partial differential equations through multifidelity machine learning.

Bruno Jacob, Amanda A. Howard, Panos Stinis

CoRR, December, 2025

Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets.

Adrienne M. Propp, Mauro Perego, Eric C. Cyr, Anthony Gruber, Amanda A. Howard, Alexander Heinlein, Panos Stinis, Daniel M. Tartakovsky

CoRR, December, 2025

Self-adaptive weighting and sampling for physics-informed neural networks.

Wenqian Chen, Amanda A. Howard, Panos Stinis

CoRR, November, 2025

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning.

Amirhossein Mollaali, Christian B. Moya, Amanda A. Howard, Alexander Heinlein, Panos Stinis, Guang Lin

CoRR, April, 2025

E-PINNs: Epistemic Physics-Informed Neural Networks.

Ashish S. Nair, Bruno Jacob, Amanda A. Howard, Ján Drgona, Panos Stinis

CoRR, March, 2025

SPIKANs: separable physics-informed Kolmogorov-Arnold networks.

Bruno Jacob, Amanda A. Howard, Panos Stinis

Mach. Learn. Sci. Technol., 2025

Multifidelity Kolmogorov-Arnold networks.

Amanda A. Howard, Bruno Jacob, Panos Stinis

Mach. Learn. Sci. Technol., 2025

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

Wenqian Chen, Amanda A. Howard, Panos Stinis

J. Comput. Phys., 2025

2024

A multifidelity approach to continual learning for physical systems.

Amanda A. Howard, Yucheng Fu, Panos Stinis

Mach. Learn. Sci. Technol., 2024

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications.

Emily Williams, Amanda A. Howard, Brek Meuris, Panos Stinis

CoRR, 2024

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

Amanda A. Howard, Bruno Jacob, Sarah H. Murphy, Alexander Heinlein, Panos Stinis

CoRR, 2024

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

Alexander Heinlein, Amanda A. Howard, Damien Beecroft, Panos Stinis

CoRR, 2024

2023

Multifidelity deep operator networks for data-driven and physics-informed problems.

Amanda A. Howard, Mauro Perego, George Em Karniadakis, Panos Stinis

J. Comput. Phys., November, 2023

A hybrid deep neural operator/finite element method for ice-sheet modeling.

QiZhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis

J. Comput. Phys., November, 2023

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

Amanda A. Howard, Sarah H. Murphy, Shady E. Ahmed, Panos Stinis

CoRR, 2023

2022

Multifidelity Deep Operator Networks.

Amanda A. Howard, Mauro Perego, George E. Karniadakis, Panos Stinis

CoRR, 2022

Machine Learning in Heterogeneous Porous Materials.

Marta D'Elia, Hang Deng, Cedric G. Fraces, Krishna C. Garikipati, Lori Graham-Brady, Amanda A. Howard, George Em Karniadakis, Vahid Keshavarzzadeh, Robert M. Kirby, J. Nathan Kutz, Chunhui Li, Xing Liu, Hannah Lu, Pania Newell, Daniel O'Malley, Masa Prodanovic, Gowri Srinivasan, Alexandre M. Tartakovsky, Daniel M. Tartakovsky, Hamdi A. Tchelepi, Bozo Vazic, Hari S. Viswanathan, Hongkyu Yoon, Piotr Zarzycki

CoRR, 2022

2021

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

Amanda A. Howard, Alexandre M. Tartakovsky

J. Comput. Phys., 2021

Physics-informed CoKriging model of a redox flow battery.

Amanda A. Howard, Alexandre M. Tartakovsky

CoRR, 2021

2020

Non-local model for surface tension in fluid-fluid simulations.

Amanda A. Howard, Alexandre M. Tartakovsky

J. Comput. Phys., 2020

Learning Unknown Physics of non-Newtonian Fluids.

Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky

CoRR, 2020