Amanda Howard

Amanda Howard

Mathematician

Publications

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

Papers

  1. Chen, Wenqian, Howard, Amanda A., & Stinis, Panos (2026). Self-adaptive weighting and sampling for physics-informed neural networks. Machine Learning: Science and Technology, 7, 025050.

  2. Williams, Emily, Howard, Amanda A., Qadeer, Saad, Meuris, Brek, & Stinis, Panos. (2026). What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications. Journal of Computational Physics, 558, 114851.

  3. Jacob, Bruno, Howard, Amanda A., & Stinis, Panos. (2025). SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks. Machine Learning: Science and Technology, 6.3, 035060.

  4. Howard, Amanda A., Jacob, Bruno, & Stinis, Panos. (2025). Multifidelity Kolmogorov-Arnold networks. Machine Learning: Science and Technology, 6.3, 035038.

  5. Chen, Wenqian, Howard, Amanda A., & Stinis, Panos (2025). Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks. Journal of Computational Physics, 542, 114226.

  6. Prange, Micah P., Govind, Niranjan, Stinis, Panagiotis, Ilton, Eugene S., Howard, Amanda A.. (2024). Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS. The Journal of Physical Chemistry A, 129, 1, 346–355.

  7. Heinlein, Alexander, Howard, Amanda A., Beecroft, Damien, & Stinis, Panos. (2025). Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems. De Gruyter Proceedings in Mathematics.

  8. Fu, Yucheng, Howard, Amanda A., Zeng, Chao, Chen, Yunxiang, Gao, Peiyuan, & Stinis, Panos. (2024). Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance. ACS Energy Letters 9, 6, 2767–2774.

  9. Howard, Amanda A., Murphy, Sarah H., Ahmed, Shady E., & Stinis, Panos. (2024). Stacked networks improve physics-informed training: applications to neural networks and deep operator networks. Foundations of Data Science, 7 134-162.

  10. Howard, Amanda A., Fu, Yucheng, & Stinis, Panos. (2024). A multifidelity approach to continual learning for physical systems. Machine Learning: Science and Technology 5.2, 025042.

  11. Howard, Amanda A., Dong, Justin, Patel, Ravi, D’Elia, Marta, Maxey, Martin R., & Stinis, Panos. (2023). Machine learning methods for particle stress development in suspension Poiseuille flows. Rheologica Acta, 1-28.

  12. Antolik, John, Howard, Amanda A., Vereda, Fernando, Ionkin, Nicolay, Maxey, Martin R., & Harris, Daniel M. (2023). Hydrodynamic irreversibility of non-Brownian suspensions in highly confined duct flow. Journal of Fluid Mechanics, 974 A11.

  13. Howard, Amanda A., Perego, Mauro, Karniadakis, George E., & Stinis, Panos. (2023). Multifidelity deep operator networks for data-driven and physics-informed problems. Journal of Computational Physics, 493, 112462.

  14. He, QiZhi, Pergo, Mauro, Howard, Amanda A., Karniadakis, George E., & Stinis, Panos. (2023). A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling. Journal of Computational Physics, 492, 112428.

  15. Singh, Rajesh K., Corbey, Jordan F., Deshmukh, Nikhil, Howard, Amanda A., Frazier, William E., Hu, Shenyang, Abrecht, David G. (2023). Computational studies of impurity migration during induction stirring of molten uranium. Computational Materials Science, 229, 11238, 26.

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

  17. 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.

  18. 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.

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

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

  21. 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.

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

  23. 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., Zolman, Nicholas, Jacob, Bruno, Brunton, Steven L., & Stinis, Panos (2026). SINDy-KANs: Sparse identification of non-linear dynamics through Kolmogorov-Arnold networks. arXiv:2603.18548.

  2. Jacob, Bruno, Howard, Amanda A., & Stinis, Panos (2025). Bridging quantum and classical computing for partial differential equations through multifidelity machine learning. arXiv:2512.05241.

  3. Propp, Adrienne, Perego, Mauro, Cyr, Eric, Gruber, Anthony, Howard, Amanda A., Heinlein, Alexander, Stinis, Panos, & Tartakovsky, Daniel. (2025). Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets. arXiv:2512.01888.

  4. Howard, Amanda A., Jacob, B., Murphy, S. H., Heinlein, A., & Stinis, P. (2024). Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems. arXiv:2406.19662.

  5. Qadeer, S., Engel, A., Howard, Amanda A., Tsou, A., Vargas, M., Stinis, P., \& Chiang, T. (2024). Efficient kernel surrogates for neural network-based regression. arXiv:2310.18612.

  6. 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.