Amanda Howard

Amanda Howard

Mathematician

Publications

You can also find my articles on my Google Scholar profile.

Papers

  1. Howard, Amanda A., Maxey, Martin R., & Gallier, Stany. (2022). A bidisperse suspension balance model. Phys. Rev. Fluids, 7, 12.

  2. Howard, Amanda A., Yu, Tong, Wang, Wei, & Tartakovsky, Alexandre M. (2022). Physics- informed CoKriging model of a redox flow battery. Journal of Power Sources 542, 231668.

  3. Howard, Amanda A., & Tartakovsky, Alexandre M. (2021). A conservative level set method for N-phase flows with a free-energy-based surface tension model. Journal of Computational Physics, 426, 109955.

  4. Reyes, Brandon, Howard, Amanda A., Perdikaris, Paris, & Tartakovsky, Alexandre M. (2021). Learning unknown physics of non-Newtonian fluids. Phys. Rev. Fluids, 6, 073301.

  5. Howard, Amanda A., & Tartakovsky, Alexandre M. (2020). Non-local model for surface tension in fluid-fluid simulations. Journal of Computational Physics, 109732.

  6. Howard, Amanda A., Maxey, Martin R., & Yeo, Kyongmin (2018). Settling of heavy particles in concentrated suspensions of neutrally buoyant particles under uniform shear. Fluid Dynamics Research, 4, 041401.

  7. Howard, Amanda A. & Maxey, Martin R. (2018). Simulation study of particle clouds in oscillating shear flow. J. Fluid Mech, 852, 484-506.

  8. Cui, Francis R., Howard, Amanda A., Maxey, Martin R. & Tripathi, Anubhav (2017). Dispersion of a suspension plug in oscillatory pressure-driven flow. Phys. Rev. Fluids, 2, 094303.

Preprints

  1. Howard, Amanda A., Perego, Mauro, Karniadakis, George E., & Stinis, Panos. (2022). Multifidelity Deep Operator Networks. arXiv, arXiv:2204.09157.

  2. Howard, Amanda A., Zhou, Yongcheng, & Tartakovsky, Alexandre M. (2019). Analytical steady-state solutions for pressure with a multiscale non-local model for two-fluid systems. arXiv:1905.08052.

Reports

  1. D’Elia, Marta, Howard, Amanda A., Kirby, Michael R., Kutz, Nathan, Tarkavoksky, Alexandre, & Viswanathan, Hari. (2021). Machine Learning in Heterogeneous Porous Materials: Discovering New Governing Equations Using Machine Learning. arXiv, arXiv:2203.04137.