Only peer-reviewed journal papers. See Google Scholar for the full list.
Group’s GitHub page: https://github.com/envfluids
bold face denotes members of our research group.
Submitted
– Lai, C.-Y., Hassanzadeh P., Sheshadri A., Sonnewald M., Ferrari R. & Balaji V., Machine learning for climate physics and simulations, [pre-print]
– Mojgani R., Waelchi D., Guan Y., Koumoustakos P. & Hassanzadeh P., Extreme event prediction with multi-agent reinforcement learning-based parametrization of atmospheric and oceanic turbulence, [pre-print]
Keywords: SGS parameterization, online/a posteriori learning, extreme events
– Sun Y. Q., Pahlavan H. A., Chattopadhyay A., Hassanzadeh P., Lubis S., Alexander M. J., Gerber E., Sheshadri A. & Guan Y., Data imbalance, Uncertainty quantification, and generalization via transfer learning in data-driven parameterizations: Lessons from the emulation of gravity wave momentum transport in WACCM, [pre-print]
Keywords: SGS parameterization, Bayesian/variational neural nets, data imbalance
– Jakhar K., Guan Y., Mojgani R., Chattopadhyay A., Hassanzadeh P. & Zanna L., Learning closed-form equations for subgrid-scale closures from high-fidelity data: Promises and challenges, [pre-print]
Keywords: equation discovery, large-eddy simulation, SGS parameterization
– Chan P., Hassanzadeh P. & Kuang Z., Response in spectra and shape of equilibrated eddies to static stability change in an idealized dry atmosphere
Keywords: atmospheric turbulence, eddy fluxes, linear response function
– Maurice M., Dasgupta R. & Hassanzadeh P., Volatile atmospheres of lava worlds
Keywords: magma ocean, exoplanets, atmosphere-interior interaction
2024
[50] Pahlavan H. A., Hassanzadeh P. & Alexander M. J., Explainable offline-online training of neural networks for parameterizations: A 1D gravity wave-QBO testbed in the small-data regime, Geophysical Research Letters, 51 (link) [pre-print]
Keywords: SGS parameterization, ensemble Kalman filter, Fourier analysis of neural nets
[49] Mojgani R., Chattopadhyay A. & Hassanzadeh P., Interpretable structural model error discovery from sparse assimilation increments using spectral bias-reduced neural networks: A quasi-geostrophic turbulence test case, Journal of Advances in Modeling Earth Systems, in press [pre-print]
Keywords: equation discovery, model error, MEDIDA
[48] Kruse C., Alexander M. J., Bramberger M., Chattopadhyay A., Hassanzadeh P., Green B., Grimsdel A. & Hoffmann L., Recreating observed convection-generated gravity waves from weather radar observations via a neural network and a dynamical atmospheric model, Journal of Advances in Modeling Earth Systems, in press [pre-print]
Keywords: gravity waves, convection, ML-based bias correction
2023
[47] Lubis S. & Hassanzadeh P., The intrinsic 150-day periodicity of the Southern Hemisphere extratropical large-scale atmospheric circulation, AGU Advances, 4 (link) [open access]
Keywords: Southern annular mode, periodicity, eddy-mean flow interaction
In the news: Rice press release, EOS editor highlight, PNNL Spotlight
[46] Subel A., Guan Y., Chattopadhyay A. & Hassanzadeh P., Explaining the physics of transfer learning in data-driven turbulence modeling, Proceedings of the National Academy of Sciences Nexus, 2 (link) [open access]
Keywords: transfer learning, data-driven SGS closure, climate change
In the news: Rice press release, IEEE Spectrum, Physics World, Popular Science
[45] Sun Y., Hassanzadeh P., Alexander M. J. & Kruse C., 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) [open access]
Keywords: data-driven SGS closure, filtering and coarse-graining, Helmholtz decomposition
[44] Chattopadhyay A., Nabizadeh E., Bach E. & Hassanzadeh P., Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems, Journal of Computational Physics, 477 (link) [pre-print]
Keywords: ensemble-based DA, Kalman filter divergence, data-driven forecast model
– Preliminary results were highlighted in SIAM News
[43] Mojgani R., Balajewicz M. & Hassanzadeh P., 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) [pre-print]
Keywords: characteristic lines, Kolmogorov n-width, PINNs’ failure modes
[42] Chattopadhyay A., Pathak J., Nabizadeh E., Bhimji W. & Hassanzadeh P., Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence, Environmental Data Science, 2 (link) [open access]
Keywords: digital twin, long-term stability, transfer learning
[41] Connolly C., Barnes E. A., Hassanzadeh P. & Pritchard M., Using neural networks to learn the jet stream forced response from natural variability, Artificial Intelligence for the Earth Systems, 2 (link) [pre-print]
Keywords: fluctuation-dissipation theorem, response function, climate change
2022
– Pathak J., Subramanian S., Harrington P., Raja S., Chattopadhyay A., Mardani M., Kurth T., Hall D., Li Z., Azizzadenesheli K., Hassanzadeh P., Kashinath K. & Anandkumar A., FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators [pre-print]
Keywords: data-driven weather forecast, Fourier neural operators, transformers
[40] Guan Y., Chattopadhyay A., Subel A. & Hassanzadeh P., 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 (link) [pre-print]
Keywords: data-driven SGS closure, large-eddy simulation, transfer learning
[39] Chattopadhyay A., Mustafa M., Hassanzadeh P., Bach E. & Kashinath K., Towards physics-inspired data-driven weather forecasting: Integrating data assimilation with a deep spatial transformer-based U-NET in a case study with ERA5, Geoscientific Model Development, 15 (link) [open access]
Preliminary findings were presented at the AI for Earth Sciences workshop at NeurIPS2020
Keywords: data-driven weather forecast, Kalman filter, transformers
[38] Nabizadeh E., Lubis S. & Hassanzadeh P., The summertime Pacific-North American weather regimes and their predictability, Geophysical Research Letters, 49 (link) [open access]
Keywords: predictability, weather regimes, attractor’s dimension and stability
[37] Guan Y., Subel A., Chattopadhyay A. & Hassanzadeh P., Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES, Physica D: Nonlinear Phenomena, 443 (link) [pre-print]
Keywords: data-driven SGS closure, equivariant deep neural nets, small-data regime
[36] Mojgani R., Chattopadhyay A. & Hassanzadeh P., Discovery of interpretable structural models by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test case, Chaos, 32 (link) [pre-print]
Keywords: structural model error, equation discovery, ensemble Kalman filter
2021
[35] Nabizadeh E., Lubis S. & Hassanzadeh P., The 3D structure of Northern Hemisphere blocking events: Climatology, role of moisture, and response to climate change, Journal of Climate, 34, 2021 (link) [pre-print]
Keywords: blocking events, weather extreme, climate change
[34] Chan P., Hassanzadeh P. & Kuang Z., Eddy length scale response to static stability change in an idealized dry atmosphere: A linear response function approach, Journal of the Atmospheric Sciences, 78, 2021 (link) [pre-print]
Keywords: eddy length scale, linear response function, climate change
[33] Khodkar M. A. & Hassanzadeh P., A data-driven, physics-informed framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings, Journal of Computational Physics, 440, 2021 (link)
Keywords: dynamic mode decomposition, spatio-temporal forecasting, chaotic dynamics
[32] Subel A., Chattopadhyay A., Guan Y. & Hassanzadeh P., 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, 2021 (link) [pre-print]
Keywords: data-driven SGS closure, large-eddy simulation, transfer learning
[31] Kashinath K., Mustafa M., Albert A., Wu J-L, Jiang C., Esmaeilzadeh S., Azizzadenesheli K., Wang R., Chattopadhyay A., Singh A., Manepalli A., Chirila D., Yu R., Walters R., White B., Xiao H., Tchelepi H.A., Marcus P.S., Anandkumar A., Hassanzadeh P. & Prabhat, Physics-informed machine learning: Case studies for weather and climate modeling, Philosophical Transactions of the Royal Society A, 379, 2021 (link) [open access]
Keywords: physics-constrained deep learning, climate modeling, spatio-temporal forecasting
[30] Lubis S. & Hassanzadeh P., An eddy-zonal flow feedback model for propagating annular modes, Journal of the Atmospheric Sciences, 78, 2021 (link) [open access]
Keywords: Southern annular mode, reduced-order model, eddy-mean flow interaction
Video: AGU 2020 talk (link, 15 min)
2020
[29] Chattopadhyay A., Subel A. & Hassanzadeh P., Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer learning, Journal of Advances in Modeling Earth Systems, 12, 2020 (link) [open access]
Keywords: data-driven SGS modeling, chaotic dynamics, transfer learning
Videos: DFD 2020 talk (link, 15 min) & SIAM MPE 2020 talk (link, 35 min)
[28] Hassanzadeh P., Lee C.-Y., Nabizadeh E., Camargo S.J., Ma D. & Yeung L.Y., Effects of climate change on the movements of future landfalling Texas tropical cyclones, Nature Communications, 11, 2020 (link) [open access]
Keywords: Texas hurricanes, steering winds, climate change
In the news: Rice press release, NSF Research News, Houston Chronicle, Fox26, phys.org, ScienceDaily & Discover Magazine
[27] Chattopadhyay A., Hassanzadeh P. & Subramanian D., Data-driven prediction of a multi-scale Lorenz 96 chaotic system using machine learning methods: Reservoir computing, artificial neural network, and long short-term memory network, Nonlinear Processes in Geophysics, 27, 2020 (link) [open access]
– Preliminary findings were presented (poster) at The International Conference on Machine Learning (ICML) workshop Climate Change: How Can AI Help?
Keywords: spatio-temporal forecasting, machine learning, chaotic dynamics
[26] Ebad Sichani M., Anarde K.A., Capshaw K.M., Padgett J.E., Meidl R.A., Hassanzadeh P., Loch-Temzelides T.P. & Bedient P.B., Hurricane risk assessment of petroleum infrastructure in a changing climate, Frontiers in Built Environment (Special topic: Worsening Tropical Cyclone Impact in Cities), 6, 2020 (link) [open access]
Keywords: Texas hurricanes, storm surge and flooding, climate change
[25] Chattopadhyay A., Nabizadeh E. & Hassanzadeh P., Analog forecasting of extreme-causing weather patterns using deep learning, Journal of Advances in Modeling Earth Systems, 2020 (link) [open access] journal’s top 10% most downloaded in 2020
In the news: Rice press release, AGU’s EOS, XSEDE featured story, deeplearning.ai The Batch, The Register, & ScienceDaily
Data: The training/testing sets used in this paper are available on Zenodo (link)
Keywords: extreme weather, analog forecasting, physics-constrained deep learning
[24] Chattopadhyay A., Hassanzadeh P. & Pasha S., Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data, Scientific Reports, 10, 2020 (link) [open access]
Keywords: weather regimes, identification and forecasting, deep learning
2019
[23] Nabizadeh E., Hassanzadeh P., Yang D. & Barnes E.A., Size of the atmospheric blocking events: Scaling law and response to climate change, Geophysical Research Letters, 46, 2019 (link) [pre-print]
Keywords: blocking events, Buckingham-PI theorem, climate change
In the news: Rice press release, NSF Research News, NASA MAP Research Highlight, phys.org, ScienceDaily & CarbonBrief
[22] Chan P., Hassanzadeh P. & Kuang Z., Evaluating indices of blocking anticyclones in terms of their linear relations with surface hot extremes, Geophysical Research Letters, 46, 2019 (link)
Keywords: blocking events, extreme weather, blocking identification
[21] Hassanzadeh P. & Kuang Z., Quantifying the annular mode dynamics in an idealized atmosphere, Journal of the Atmospheric Sciences, 76, 2019 (link) [pre-print]
Keywords: annular modes, linear response function, eddy-mean flow interaction
[20] Khodkar M. A., Hassanzadeh P., Nabi S. & Grover P., Reduced-order modeling of fully turbulent buoyancy-driven flows using the Green’s function method, Physical Review Fluids, 4, 2019 (link) [pre-print] Selected for Editors’ Suggestion
Keywords: reduced-order model, linear response function, eddy-mean flow interaction
[19] Pasha S., Hassanzadeh P., Ecker M. & Ho-Fung V., A hierarchical classification of adolescent idiopathic scoliosis: Identifying the distinguishing features in 3D spinal deformities, PLoS One, 14, 2019 (link) [open access]
2018
[18] Khodkar M.A. & Hassanzadeh P., Data-driven reduced modelling of turbulent Rayleigh-Benard convection using DMD-enhanced Fluctuation-Dissipation Theorem, Journal of Fluid Mechanics, 852, 2018 (link) [pre-print]
[17] Ronalds B., Barnes E.A. & Hassanzadeh P., A barotropic mechanism for the response of jet stream variability to Arctic Amplification and sea ice loss, Journal of Climate, 2018 (link) [open access]
2017
[16] Anderson B.T., Hassanzadeh P. & Caballero R., Persistent anomalies of the extratropical Northern Hemisphere wintertime circulation as an initiator of El Niño/Southern Oscillation events, Scientific Reports, 7, 2017 (link) [open access]
[15] Jeevanjee N., Hassanzadeh P., Hill S. & Sheshadri A., A perspective on climate model hierarchies, Journal of Advances in Modeling Earth Systems, 9, 2017 (link) [open access]
[14] Ma D., Hassanzadeh P. & Kuang Z., Quantifying the eddy-jet feedback strength of the annular mode in an idealized GCM and reanalysis data, Journal of the Atmospheric Sciences, 74, 2017 (link) [open access]
[13] Mahdinia M., Hassanzadeh P., Marcus P.S. & Jiang C.-H., Stability of 3D Gaussian vortices in an unbounded, rotating, vertically-stratified Boussinesq flow: Linear analysis, Journal of Fluid Mechanics, 824, 2017 (link) [pre-print]
2016
[12] Hassanzadeh P. & Kuang Z., The linear response function of an idealized atmosphere: Part II: Implications for the practical use of the Fluctuation-Dissipation Theorem and the role of operator’s nonnormality, Journal of the Atmospheric Sciences, 73, 2016 (link) [open access]
[11] Hassanzadeh P. & Kuang Z., The linear response function of an idealized atmosphere. Part I: Construction using Green’s functions and applications, Journal of the Atmospheric Sciences, 73, 2016 (link) [open access]
2015
[10] Hassanzadeh P. & Kuang Z., Blocking variability: Arctic Amplification versus Arctic Oscillation, Geophysical Research Letters, 42, 2015 (link) [open access]
[9] Marcus P.S., Pei S., Jiang C.-H., Barranco J.A., Hassanzadeh P. & Lecoanet D., Zombie vortex instability. I. A purely hydrodynamic instability to resurrect the dead zones of protoplanetary disks, The Astrophysical Journal, 808, 2015 (link) [pre-print]
2014
[8] Hassanzadeh P., Kuang Z. & Farrell B.F., Responses of midlatitude blocks and wave amplitude to changes in the meridional temperature gradient in an idealized dry GCM, Geophysical Research Letters, 41, 2014 (link) Selected for AGU Research Spotlight and Editor’s Highlights
[7] Hassanzadeh P., Chini G.P. & Doering C.R., Wall to wall optimal transport, Journal of Fluid Mechanics, 751, 2014 (link) [pre-print]
2008-2013
[6] Marcus P.S., Pei S., Jiang C.-H. & Hassanzadeh P., Three-dimensional vortices generated by self-replication in stably stratified rotating shear flows, Physical Review Letters, 111, 2013 (link)
In the news: Berkeley News Center, ScienceDaily, and other news outlets
[5] Hassanzadeh P., Marcus P.S. & Le Gal P., The universal aspect ratio of vortices in rotating stratified flows: theory and simulation, Journal of Fluid Mechanics, 706, 2012 (link)
[4] Hassanzadeh P. & Raithby G.D., Efficient iterative solution of the P1 equation, Journal of Heat Transfer, 131, 2009 (link)
[3] Hassanzadeh P., Raithby G.D. & Chui E.H., Efficient calculation of radiation heat transfer in anisotropically scattering media using the QL method, Journal of Computational Thermal Sciences, 1, 2009 (link)
[2] Hassanzadeh P. & Raithby G.D., Application of the finite volume method to the second-order radiative transfer equation: accuracy and solution cost, Journal of Numerical Heat Transfer-B, 53, 2008 (link)
[1] Hassanzadeh P., Raithby G.D. & Chui E.H., The efficient calculation of radiation heat transfer in participating media, Journal of Thermophysics and Heat Transfer, 22, 2008 (link)