Publications
We reject: kings, presidents and voting. We believe in: rough consensus and running code. --David Clark, Internet Engineering Task Force
We reject: kings, presidents and voting. We believe in: rough consensus and running code. --David Clark, Internet Engineering Task Force
For a full list, check my Google Scholar
Peer-reviewed Journals (accepted/published/in-press)
Lupin-Jimenez, L., Darman, M., Hazarika, S., Wu, T., Gray, M., He, R., Wong, A., & Chattopadhyay, A. (2025). Simultaneous emulation and downscaling with physically consistent deep learning-based regional ocean emulators. Journal of Geophysical Research: Machine Learning and Computation, 2(3), e2025JH000851.
Sun, Y. Q., Hassanzadeh, P., Zand, M., Chattopadhyay, A., Weare, J., & Abbot, D. S. (2025). Can AI weather models predict out-of-distribution gray swan tropical cyclones? Proceedings of the National Academy of Sciences, 122(21), e2420914122.
Lowe, A. B., Gray, M., Chattopadhyay, A., Wu, T., & He, R. (2025). Long-term predictions of Loop Current Eddy evolutions using OceanNet: A Fourier neural-operator-based data-driven ocean emulator. Artificial Intelligence for the Earth Systems.
Gray, M., Chattopadhyay, A., Wu, T., Lowe, A., & He, R. (2025). Long-term prediction of the Gulf Stream meander using OceanNet: A principled neural-operator-based digital twin. Ocean Science, 21(3), 1065–1080.
Darman, M., Hassanzadeh, P., Zanna, L., & Chattopadhyay, A. (2025). Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence. arXiv preprint arXiv:2504.15487.
Guan, H., Arcomano, T., Chattopadhyay, A., & Maulik, R. (2025). LUCIE-3D: A three-dimensional climate emulator for forced responses. arXiv preprint arXiv:2509.02061.
Asefi, N., Lupin-Jimenez, L., Wu, T., He, R., & Chattopadhyay, A. (2025). Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity. Machine Learning: Earth.
Jakhar, K., Guan, Y., Mojgani, R., Chattopadhyay, A., & Hassanzadeh, P. (2024). Learning closed-form equations for subgrid-scale closures from high-fidelity data: Promises and challenges. Journal of Advances in Modeling Earth Systems, 16(7), e2023MS003874.
Sun, Y. Q., Pahlavan, H. A., Chattopadhyay, A., Hassanzadeh, P., Lubis, S. W., Alexander, M. J., Gerber, E. P., Sheshadri, A., & Guan, Y. (2024). Data imbalance, uncertainty quantification, and transfer learning in data-driven parameterizations: Lessons from the emulation of gravity wave momentum transport in WACCM. Journal of Advances in Modeling Earth Systems, 16(7), e2023MS004145.
Mojgani, R., Chattopadhyay, A., & Hassanzadeh, P. (2024). Interpretable structural model error discovery from sparse assimilation increments using spectral bias-reduced neural networks. Journal of Advances in Modeling Earth Systems, 16(3), e2023MS004033.
Chattopadhyay, A., Gray, M., Wu, T., Lowe, A., & He, R. (2024). OceanNet: A principled neural operator-based digital twin for regional oceans. Scientific Reports, 14(1), 21181.
Rodriguez, A., Chattopadhyay, A., Kumar, P., Rodriguez, L. F., & Kumar, V. (2024). Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An unsupervised framework for physics-informed domain decomposition and mixtures of experts. arXiv preprint arXiv:2412.06842.
Guan, H., Arcomano, T., Chattopadhyay, A., & Maulik, R. (2024). Lucie: A lightweight uncoupled climate emulator with long-term stability and physical consistency for O(1000)-member ensembles. Journal of Advances in Modeling Earth Systems. arXiv preprint arXiv:2405.16297.
Chakraborty, D., Chung, S. W., Chattopadhyay, A., & Maulik, R. (2024). Improved deep learning of chaotic dynamical systems with multistep penalty losses. arXiv preprint arXiv:2410.05572.
Mohan, A., Chattopadhyay, A., & Miller, J. (2024). What You See is Not What You Get: Neural Partial Differential Equations and the Illusion of Learning. arXiv preprint arXiv:2411.15101.
Chattopadhyay, A., Sun, Y. Q., & Hassanzadeh, P. (2023). Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution. arXiv preprint arXiv:2304.*
Subel, A., Guan, Y., Chattopadhyay, A., & Hassanzadeh, P. (2023). Explaining the physics of transfer learning in data-driven turbulence modeling. PNAS Nexus, 2(3), pgad015.
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, 111918.
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, e1.
Kruse, C. G., Alexander, M. J., Bramberger, M., Chattopadhyay, A., & Hassanzadeh, P. (2023). 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
Chattopadhyay, A., Mustafa, M., Hassanzadeh, P., Bach, E., & Kashinath, K. (2022). 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(5), 2221–2237.
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.
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, 32(6).
Kashinath, K., Mustafa, M., Albert, A., Wu, J. L., Jiang, C., Azizzadenesheli, K., Wang, R., Chattopadhyay, A., & others. (2022). Physics-informed machine learning: Case studies for weather and climate modelling. Philosophical Transactions of the Royal Society A, 379(2194), 20200093.
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).
Mondal, S., Chattopadhyay, A., Mukhopadhyay, A., & Ray, A. (2021). Transfer learning of deep neural networks for predicting thermoacoustic instabilities in combustion systems. Energy and AI, 5, 100085.
Chattopadhyay, A., Subel, A., & Hassanzadeh, P. (2020). Data-driven super-parameterization using deep learning: Experimentation with multiscale Lorenz 96 systems and transfer learning. Journal of Advances in Modeling Earth Systems, 12(11), e2020MS002084.
Chattopadhyay, A., Hassanzadeh, P., & Subramanian, D. (2020). Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM. Nonlinear Processes in Geophysics, 27, 1–26.
Chattopadhyay, A., Hassanzadeh, P., & Pasha, S. (2020). Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data. Scientific Reports, 10, 1317.
Chattopadhyay, A., Nabizadeh, E., & Hassanzadeh, P. (2020). Analog forecasting of extreme-causing weather patterns using deep learning. Journal of Advances in Modeling Earth Systems, 12(2).
Chattopadhyay, A., Mustafa, M., Hassanzadeh, P., & Kashinath, K. (2020). Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence. Proceedings of ACM.
Kotteda, V. M. K., Schiaffino, A., Chattopadhyay, A., Shantha-Kumar, S., Kumar, V., & Bronson, A. (2019). Sensitivity of viscosity on molten Ti infusion into a B4C-packed bed at the microscale. Metallurgical and Materials Transactions B, 50(4), 1559–1565.
Jakhar, K., Chattopadhyay, A., Thakur, A., & Raj, R. (2019). Spline-based interface modeling and optimization (SIMO) for surface tension and contact angle measurements. arXiv preprint arXiv:1909.05943.
Jakhar, K., Chattopadhyay, A., Thakur, A., & Raj, R. (2017). Spline-based shape prediction and analysis of uniformly rotating sessile and pendant droplets. Langmuir, 33(22), 5603–5612.
Peer-reviewed Conferences
Computational Study of High Temperature Liquid Metal Infusion: Fluid Engineering Division Summer Meeting 2017 see paper
Data-driven surrogate models for climate modeling: application of echo state networks to the multi-scale Lorenz system as a test case: 36th International Conference on Machine Learning, CCAI workshop see paper
Identifying Clustered Weather Patterns Using a Deep Convolutional Neural Network: A Test Case: 8th International Workshop on Climate Informatics, Colorado, 2018, Boulder see proceedings
Leveraging Trilinos's Next Generation Computing Framework for an Exa-Scale Poro-Elastic Network Simulator Implementation: IEEE High Performance Extreme Computing 2016
Next Generation Exa-Scale Capable Multiphase Solver With Trilinos: ASME International Mechanical Engineering Congress and Exposition 2016, see paper
Spline Based Modeling of Two-dimensional droplets on Rough and Heterogeneous Surfaces: International Conference of Fluid Mechanics and Fluid Power, 2015. Also, a book chapter in Springer, Fluid Mechanics and Fluid Power: Contemporary Research (Book)
Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence, International Conference on Climate Informatics, Oxford UK., 2020. Recommended as top 15% of accepted submissions. paper
A Framework to Integrate MFiX with Trilinos for High Fidelity Fluidized Bed Computations: IEEE High Performance Extreme Computing 2016 see paper