Nguyen, N. C., & Peraire, J. (2023). Efficient and accurate nonlinear model reduction via first-order empirical interpolation. Journal of Computational Physics, 112512.
Journal
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Nguyen, N. C., Vila-Pérez, J., & Peraire, J. (2023). An adaptive viscosity regularization approach for the numerical solution of conservation laws: Application to finite element methods. Journal of Computational Physics, 112507.
Nguyen, N. C., Vila-Pérez, J., & Peraire, J. (2023). An adaptive viscosity regularization approach for the numerical solution of conservation laws: Application to finite element methods. Journal of Computational Physics, 112507.
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Nguyen, N. C., & Rohskopf, A. (2023). Proper orthogonal descriptors for efficient and accurate interatomic potentials. Journal of Computational Physics, 480, 112030.
Nguyen, N. C., & Rohskopf, A. (2023). Proper orthogonal descriptors for efficient and accurate interatomic potentials. Journal of Computational Physics, 480, 112030.
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Nguyen, N. C. (2023). Fast proper orthogonal descriptors for many-body interatomic potentials. Physical Review B, 107(14), 144103.
Nguyen, N. C. (2023). Fast proper orthogonal descriptors for many-body interatomic potentials. Physical Review B, 107(14), 144103.
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Rohskopf, A., Goff, J., Sema, D., Gordiz, K., Nguyen, N. C., Henry, A., Thompson, A. P., & Wood, M. A. (2023). Exploring model complexity in machine learned potentials for simulated properties. Journal of Materials Research
Rohskopf, A., Goff, J., Sema, D., Gordiz, K., Nguyen, N. C., Henry, A., Thompson, A. P., & Wood, M. A. (2023). Exploring model complexity in machine learned potentials for simulated properties. Journal of Materials Research.
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Vila-Pérez, J., Van Heyningen, R. L., Nguyen, N.-C., & Peraire, J. (2022). Exasim: Generating discontinuous Galerkin codes for numerical solutions of partial differential equations on graphics processors. SoftwareX, 20, 101212.
Vila-Pérez, J., Van Heyningen, R. L., Nguyen, N.-C., & Peraire, J. (2022). Exasim: Generating discontinuous Galerkin codes for numerical solutions of partial differential equations on graphics processors. SoftwareX, 20, 101212.
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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
Nandy, A., Duan, C., Taylor, M.G., Liu, F., Steeves, A.H., & Kulik, H.J. (2021). Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning. Chemical Reviews, 121, 9927–10000.