Mathematician
Mathematician

Biography

Amanda Howard specializes in computational fluid dynamics and scientific machine learning. She received her PhD in applied mathematics from Brown University in 2018 and her BS in mathematics from Stanford University in 2012. Her PhD research focused on computational methods for fluid-solid multiphase flows. Her current research includes scientific machine learning methods for cases with limited data and multiphase flow modeling with nonlocal models.

Disciplines and Skills

  • 3D simulation
  • Applied mathematics
  • Computational fluid dynamics
  • Computational mathematics
  • Fluid simulation
  • Machine learning
  • Multiphase flow modeling
  • Multiphysics modeling

Education

  • PhD in applied mathematics, Brown University
  • MS in applied mathematics, Brown University
  • BS in mathematics, Stanford University

Awards and Recognitions

  • Presenter, Rising Stars Computational and Data Sciences Workshop, 2019

Publications

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. 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
  • 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

  • Howard A.A., and A.M. Tartakovsky. 2021. “A conservative level set method for N-phase flows with a free-energy-based surface tension model.” Journal of Computational Physics 426. PNNL-SA-149622. doi:10.1016/j.jcp.2020.109955
  • Reyes B.C., A.A. Howard, P. Perdikaris, and A.M. Tartakovsky. 2021. “Learning Unknown Physics of non-Newtonian Fluids.” Physical Review Fluids 6, no. 7:073301. PNNL-SA-155554. doi:10.1103/PhysRevFluids.6.073301

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