Learning Unknown Physics of non-Newtonian Fluids. - Brandon Reyes; Amanda A. Howard; Paris Perdikaris; Alexandre M. Tartakovsky (CoRR, 2020)

Non-local model for surface tension in fluid-fluid simulations. - Amanda A. Howard; Alexandre M. Tartakovsky (J. Comput. Phys., 2020)

Physics-informed CoKriging model of a redox flow battery. - Amanda A. Howard; Alexandre M. Tartakovsky (CoRR, 2021)

A conservative level set method for N-phase flows with a free-energy-based surface tension model. - Amanda A. Howard; Alexandre M. Tartakovsky (J. Comput. Phys., 2021)

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; et al. (CoRR, 2022)

Multifidelity Deep Operator Networks. - Amanda A. Howard; Mauro Perego; George E. Karniadakis; Panos Stinis (CoRR, 2022)

A multifidelity approach to continual learning for physical systems. - Amanda A. Howard; Yucheng Fu; Panos Stinis (CoRR, 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)

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., 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., 2023)

Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems. - Alexander Heinlein; Amanda A. Howard; Damien Beecroft; 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)

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 (CoRR, 2024)

Multifidelity Kolmogorov-Arnold Networks. - Amanda A. Howard; Bruno Jacob; Panos Stinis (CoRR, 2024)

SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks. - Bruno Jacob; Amanda A. Howard; Panos Stinis (CoRR, 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)

E-PINNs: Epistemic Physics-Informed Neural Networks. - Ashish S. Nair; Bruno Jacob; Amanda A. Howard; Jan Drgona; Panos Stinis (CoRR, 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 001 (CoRR, 2025)