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Fundamental and Computational Sciences Directorate

Staff information

Amanda Howard

Scientific Machine Learning
Mathematician

PNNL Publications

2025

  • Howard A.A., S.H. Murphy, S. Ahmed, and P. Stinis. 2025. "Stacked networks improve physics-informed training: applications to neural networks and deep operator networks." Foundations of Data Science 7, no. 1:134-162. PNNL-SA-192288. doi:10.3934/fods.2024029

2024

  • Fu Y., A.A. Howard, C. Zeng, Y. Chen, P. Gao, and P. Stinis. 2024. "Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance." ACS Energy Letters 9, no. 6:2767-2774. PNNL-SA-192565. doi:10.1021/acsenergylett.4c00493
  • Howard A.A., Y. Fu, and P. Stinis. 2024. "A multifidelity approach to continual learning for physical systems." Machine Learning: Science and Technology 5, no. 2:Art No. 25042. PNNL-SA-198022. doi:10.1088/2632-2153/ad45b2

2023

  • Antolik J.T., A.A. Howard, F. Vereda, N. Ionkin, M. Maxey, and D. Harris. 2023. "Hydrodynamic irreversibility of non-Brownian suspensions in highly confined duct flow." Journal of Fluid Mechanics 974. PNNL-SA-181972. doi:10.1017/jfm.2023.793
  • He Q., M. Perego, A.A. Howard, G.E. Karniadakis, G.E. Karniadakis, and P. Stinis. 2023. "A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling." Journal of Computational Physics 492. PNNL-SA-181082. doi:10.1016/j.jcp.2023.112428
  • Howard A.A., J. Dong, R. Patel, M. D'Elia, M. Maxey, and P. Stinis. 2023. "Machine learning methods for particle stress development in suspension Poiseuille flows." Rheologica Acta 62, no. 10:507-534. PNNL-SA-182934. doi:10.1007/s00397-023-01413-z
  • Howard A.A., M. Perego, G.E. Karniadakis, G.E. Karniadakis, and P. Stinis. 2023. "Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems." Journal of Computational Physics 493. PNNL-SA-172145. doi:10.1016/j.jcp.2023.112462
  • Prange M.P., N. Govind, P. Stinis, E.S. Ilton, and A.A. Howard. 2023. A Multifidelity and Multimodal Machine Learning Approach for Extracting Bonding Environments of Impurities and Dopants from X-ray Spectroscopies. PNNL-34996. Richland, WA: Pacific Northwest National Laboratory. A Multifidelity and Multimodal Machine Learning Approach for Extracting Bonding Environments of Impurities and Dopants from X-ray Spectroscopies
  • Singh R.K., J.F. Corbey, N.S. Deshmukh, A.A. Howard, W.E. Frazier, S. Hu, and D. Abrecht. 2023. "Computational studies of impurity migration during induction stirring of molten uranium." Computational Materials Science 229. PNNL-SA-182708. doi:10.1016/j.commatsci.2023.112386

2022

  • Howard A.A., M. Maxey, and S. Gallier. 2022. "Bidisperse suspension balance model." Physical Review Fluids 7, no. 12:Art. No. 124301. PNNL-SA-171996. doi:10.1103/PhysRevFluids.7.124301
  • Howard A.A., T. Yu, W. Wang, and A.M. Tartakovsky. 2022. "Physics-informed CoKriging model of a redox flow battery." Journal of Power Sources 542. PNNL-SA-162807. doi:10.1016/j.jpowsour.2022.231668

2021

2020

  • Howard A.A., and A.M. Tartakovsky. 2020. "Non-local model for surface tension in fluid-fluid simulations." Journal of Computational Physics 421. PNNL-SA-144669. doi:10.1016/j.jcp.2020.109732

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