Publications

Guan, Y., Subel, A., Chattopadhyay, A. and Hassanzadeh, P. (2023). Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES. Physica D: Nonlinear Phenomenas, 443, p.133568. link

Subel, A., Guan, Y., Chattopadhyay, A., & Hassanzadeh, P. (2023). Explaining the physics of transfer learning in data-driven turbulence modeling. Proceedings of the National Academy of Sciences Nexus, 2. link

Sun, Y., Hassanzadeh, P., Alexander, M. J., & Kruse, C. (2023). Quantifying 3D gravity wave drag in a library of tropical convection-permitting simulations for data-driven parameterizations. Journal of Advances in Modeling Earth Systems, 15. link

Chattopadhyay, A., Nabizadeh, E., Bach, E., & Hassanzadeh P. (2023). Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems. Journal of Computational Physics, 477. link

Mojgani, R., Balajewicz, M., & Hassanzadeh, P. (2023). Kolmogorov n–width and Lagrangian physics–informed neural networks: A causality–conforming manifold for convection–dominated PDEs. Computer Methods in Applied Mechanics and Engineering, 404. link

Chattopadhyay, A., Pathak, J., Nabizadeh, E., Bhimji, W., & Hassanzadeh P. (2023). Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence. Environmental Data Science, 2. link

Guan, Y., Chattopadhyay, A., Subel, A., & Hassanzadeh, P. (2022). Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning. Journal of Computational Physics, 458, 111090. link

Mansfield, L. A., & Sheshadri, A. (2022). Calibration and Uncertainty Quantification of a Gravity Wave Parameterization: A Case Study of the Quasi‐Biennial Oscillation in an Intermediate Complexity Climate Model. Journal of Advances in Modeling Earth Systems, 14(11), e2022MS003245. link

Chattopadhyay, A., Pathak, J., Nabizadeh, E., Bhimji, W., & Hassanzadeh, P. (2022). Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence. in press at Environmental Data Science. link

Kruse, C. G., Bacmeister, J. T., Zarzycki, C. M., Larson, V. E., & Thayer-Calder, K. (2022). Do Nudging Tendencies Depend on the Nudging Timescale Chosen in Atmospheric Models? Journal of Advances in Modeling Earth Systems, 14, e2022MS003024. link

Mojgani, R., Chattopadhyay, A., & Hassanzadeh, P. (2022). Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto–Sivashinsky test case. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(6), 061105. link

Espinosa, Z. I., Sheshadri, A., Cain, G. R., Gerber, E. P., & DallaSanta, K. J. (2022). Machine learning gravity wave parameterization generalizes to capture the QBO and response to increased CO2. Geophysical Research Letters, 49, e2022GL098174. link

Kruse, C. G., Alexander, M. J., Hoffmann, L., van Niekerk, A., Polichtchouk, I., Bacmeister, J. T., Holt, L., Plougonven, R., Šácha, P., Wright, C., Sato, K., Shibuya, R., Gisinger, S., Ern, M., Meyer, C. I., & Stein, O. (2022). Observed and Modeled Mountain Waves from the Surface to the Mesosphere near the Drake Passage. Journal of the Atmospheric Sciences, 79(4), 909-932. link

Subel, A., Chattopadhyay, A., Guan, Y., & Hassanzadeh, P. (2021). Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning. Physics of Fluids, 33(3), 031702. link

Lindgren, E. A., Sheshadri, A., Podglajen, A., & Carver, R. W. (2020). Seasonal and Latitudinal Variability of the Gravity Wave Spectrum in the Lower Stratosphere. Journal of Geophysical Research: Atmospheres, 125(18), e2020JD032850. link