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2020 – today
- 2025
[j5]Wenqian Chen, Amanda A. Howard, Panos Stinis:
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks. J. Comput. Phys. 542: 114226 (2025)- [i15]Ashish S. Nair, Bruno Jacob
, Amanda A. Howard, Jan Drgona, Panos Stinis:
E-PINNs: Epistemic Physics-Informed Neural Networks. CoRR abs/2503.19333 (2025) - [i14]Amirhossein Mollaali, Christian B. Moya, Amanda A. Howard, Alexander Heinlein, Panos Stinis, Guang Lin:
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning. CoRR abs/2504.15240 (2025) - 2024
- [i13]Alexander Heinlein, Amanda A. Howard, Damien Beecroft, Panos Stinis:
Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems. CoRR abs/2401.07888 (2024) - [i12]Amanda A. Howard, Bruno Jacob, Sarah H. Murphy, Alexander Heinlein, Panos Stinis:
Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems. CoRR abs/2406.19662 (2024) - [i11]Wenqian Chen, Amanda A. Howard, Panos Stinis:
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks. CoRR abs/2407.01613 (2024) - [i10]Amanda A. Howard, Bruno Jacob, Panos Stinis:
Multifidelity Kolmogorov-Arnold Networks. CoRR abs/2410.14764 (2024) - [i9]Bruno Jacob, Amanda A. Howard, Panos Stinis:
SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks. CoRR abs/2411.06286 (2024) - [i8]Emily Williams, Amanda A. Howard, Brek Meuris, Panos Stinis:
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications. CoRR abs/2411.18459 (2024) - 2023
- [j4]QiZhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis:
A hybrid deep neural operator/finite element method for ice-sheet modeling. J. Comput. Phys. 492: 112428 (2023) - [j3]Amanda A. Howard, Mauro Perego, George Em Karniadakis, Panos Stinis:
Multifidelity deep operator networks for data-driven and physics-informed problems. J. Comput. Phys. 493: 112462 (2023) - [i7]QiZhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis:
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling. CoRR abs/2301.11402 (2023) - [i6]Amanda A. Howard, Yucheng Fu, Panos Stinis:
A multifidelity approach to continual learning for physical systems. CoRR abs/2304.03894 (2023) - [i5]Amanda A. Howard, Sarah H. Murphy, Shady E. Ahmed, Panos Stinis:
Stacked networks improve physics-informed training: applications to neural networks and deep operator networks. CoRR abs/2311.06483 (2023) - 2022
- [i4]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:
Machine Learning in Heterogeneous Porous Materials. CoRR abs/2202.04137 (2022) - [i3]Amanda A. Howard, Mauro Perego, George E. Karniadakis, Panos Stinis:
Multifidelity Deep Operator Networks. CoRR abs/2204.09157 (2022) - 2021
- [j2]Amanda A. Howard, Alexandre M. Tartakovsky:
A conservative level set method for N-phase flows with a free-energy-based surface tension model. J. Comput. Phys. 426: 109955 (2021) - [i2]Amanda A. Howard, Alexandre M. Tartakovsky:
Physics-informed CoKriging model of a redox flow battery. CoRR abs/2106.09188 (2021) - 2020
- [j1]Amanda A. Howard, Alexandre M. Tartakovsky:
Non-local model for surface tension in fluid-fluid simulations. J. Comput. Phys. 421: 109732 (2020) - [i1]Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky:
Learning Unknown Physics of non-Newtonian Fluids. CoRR abs/2009.01658 (2020)
Coauthor Index
aka: Panos Stinis
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