Amanda A. Howard

28 papers Journal 28
YearRankTypeTitle / Venue / Authors
2026 J jnl
SINDy-KANs: Sparse identification of non-linear dynamics through Kolmogorov-Arnold networks.
CoRR
Amanda A. Howard, Nicholas Zolman, Bruno Jacob, Steven L. Brunton, Panos Stinis
2026 J jnl
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications.
J. Comput. Phys.
Emily Williams, Amanda A. Howard, Saad Qadeer, Brek Meuris, Panos Stinis
2025 J jnl
Bridging quantum and classical computing for partial differential equations through multifidelity machine learning.
CoRR
Bruno Jacob, Amanda A. Howard, Panos Stinis
2025 J jnl
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning.
CoRR
Amirhossein Mollaali, Christian B. Moya, Amanda A. Howard, Alexander Heinlein, Panos Stinis, Guang Lin
2025 J jnl
Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets.
CoRR
Adrienne M. Propp, Mauro Perego, Eric C. Cyr, Anthony Gruber, Amanda A. Howard, Alexander Heinlein, Panos Stinis, Daniel M. Tartakovsky
2025 J jnl
E-PINNs: Epistemic Physics-Informed Neural Networks.
CoRR
Ashish S. Nair, Bruno Jacob, Amanda A. Howard, Ján Drgona, Panos Stinis
2025 J jnl
Multifidelity Kolmogorov-Arnold networks.
Mach. Learn. Sci. Technol.
Amanda A. Howard, Bruno Jacob, Panos Stinis
2025 J jnl
SPIKANs: separable physics-informed Kolmogorov-Arnold networks.
Mach. Learn. Sci. Technol.
Bruno Jacob, Amanda A. Howard, Panos Stinis
2025 J jnl
Self-adaptive weighting and sampling for physics-informed neural networks.
CoRR
Wenqian Chen, Amanda A. Howard, Panos Stinis
2025 J jnl
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks.
J. Comput. Phys.
Wenqian Chen, Amanda A. Howard, Panos Stinis
2024 J jnl
A multifidelity approach to continual learning for physical systems.
Mach. Learn. Sci. Technol.
Amanda A. Howard, Yucheng Fu, Panos Stinis
2024 J jnl
Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems.
CoRR
Amanda A. Howard, Bruno Jacob, Sarah H. Murphy, Alexander Heinlein, Panos Stinis
2024 J jnl
Multifidelity Kolmogorov-Arnold Networks.
CoRR
Amanda A. Howard, Bruno Jacob, Panos Stinis
2024 J jnl
Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems.
CoRR
Alexander Heinlein, Amanda A. Howard, Damien Beecroft, Panos Stinis
2024 J jnl
SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks.
CoRR
Bruno Jacob, Amanda A. Howard, Panos Stinis
2024 J jnl
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks.
CoRR
Wenqian Chen, Amanda A. Howard, Panos Stinis
2024 J jnl
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications.
CoRR
Emily Williams, Amanda A. Howard, Brek Meuris, Panos Stinis
2023 J jnl
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling.
CoRR
Qizhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis
2023 J jnl
A hybrid deep neural operator/finite element method for ice-sheet modeling.
J. Comput. Phys.
Qizhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis
2023 J jnl
A multifidelity approach to continual learning for physical systems.
CoRR
Amanda A. Howard, Yucheng Fu, Panos Stinis
2023 J jnl
Multifidelity deep operator networks for data-driven and physics-informed problems.
J. Comput. Phys.
Amanda A. Howard, Mauro Perego, George Em Karniadakis, Panos Stinis
2023 J jnl
Stacked networks improve physics-informed training: applications to neural networks and deep operator networks.
CoRR
Amanda A. Howard, Sarah H. Murphy, Shady E. Ahmed, Panos Stinis
2022 J jnl
Machine Learning in Heterogeneous Porous Materials.
CoRR
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
2022 J jnl
Multifidelity Deep Operator Networks.
CoRR
Amanda A. Howard, Mauro Perego, George E. Karniadakis, Panos Stinis
2021 J jnl
A conservative level set method for
J. Comput. Phys.
Amanda A. Howard, Alexandre M. Tartakovsky
2021 J jnl
Physics-informed CoKriging model of a redox flow battery.
CoRR
Amanda A. Howard, Alexandre M. Tartakovsky
2020 J jnl
Learning Unknown Physics of non-Newtonian Fluids.
CoRR
Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky
2020 J jnl
Non-local model for surface tension in fluid-fluid simulations.
J. Comput. Phys.
Amanda A. Howard, Alexandre M. Tartakovsky