A framework to integrate MFiX with Trilinos for high fidelity fluidized bed computations. - V. M. Krushnarao Kotteda; Ashesh Chattopadhyay; Vinod Kumar 015; William F. Spotz (HPEC, 2016)

A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns. - Ashesh Chattopadhyay; Pedram Hassanzadeh; Saba Pasha (CoRR, 2018)

Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM. - Ashesh Chattopadhyay; Pedram Hassanzadeh; Krishna V. Palem; Devika Subramanian (CoRR, 2019)

Analog forecasting of extreme-causing weather patterns using deep learning. - Ashesh Chattopadhyay; Ebrahim Nabizadeh; Pedram Hassanzadeh (CoRR, 2019)

Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence. - Ashesh Chattopadhyay; Mustafa Mustafa; Pedram Hassanzadeh; Karthik Kashinath (CI, 2020)

Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers. - Ashesh Chattopadhyay; Mustafa Mustafa; Pedram Hassanzadeh; Eviatar Bach; Karthik Kashinath (CoRR, 2021)

Closed-form discovery of structural errors in models of chaotic systems by integrating Bayesian sparse regression and data assimilation. - Rambod Mojgani; Ashesh Chattopadhyay; Pedram Hassanzadeh (CoRR, 2021)

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. - Jaideep Pathak; Shashank Subramanian; Peter Harrington; Sanjeev Raja; Ashesh Chattopadhyay; Morteza Mardani; Thorsten Kurth; David Hall; Zongyi Li; Kamyar Azizzadenesheli; Pedram Hassanzadeh; Karthik Kashinath; et al. (CoRR, 2022)

Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence. - Ashesh Chattopadhyay; Jaideep Pathak; Ebrahim Nabizadeh; Wahid Bhimji; Pedram Hassanzadeh (CoRR, 2022)

Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow. - Adam Subel; Yifei Guan; Ashesh Chattopadhyay; Pedram Hassanzadeh (CoRR, 2022)

Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning. - Yifei Guan; Ashesh Chattopadhyay; Adam Subel; Pedram Hassanzadeh (J. Comput. Phys., 2022)

Long-term instabilities of deep learning-based digital twins of the climate system: The cause and a solution. - Ashesh Chattopadhyay; Pedram Hassanzadeh (CoRR, 2023)

Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges. - Karan Jakhar; Yifei Guan; Rambod Mojgani; Ashesh Chattopadhyay; Pedram Hassanzadeh; Laura Zanna (CoRR, 2023)

OceanNet: A principled neural operator-based digital twin for regional oceans. - Ashesh Chattopadhyay; Michael Gray; Tianning Wu; Anna B. Lowe; Ruoying He (CoRR, 2023)

Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems. - Ashesh Chattopadhyay; Ebrahim Nabizadeh; Eviatar Bach; Pedram Hassanzadeh (J. Comput. Phys., 2023)

LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles. - Haiwen Guan; Troy Arcomano; Ashesh Chattopadhyay; Romit Maulik (CoRR, 2024)

Improved deep learning of chaotic dynamical systems with multistep penalty losses. - Dibyajyoti Chakraborty; Seung Whan Chung; Ashesh Chattopadhyay; Romit Maulik (CoRR, 2024)

Can AI weather models predict out-of-distribution gray swan tropical cyclones? - Y. Qiang Sun; Pedram Hassanzadeh; Mohsen Zand; Ashesh Chattopadhyay; Jonathan Weare; Dorian S. Abbot (CoRR, 2024)

What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning. - Arvind Mohan; Ashesh Chattopadhyay; Jonah Miller (CoRR, 2024)

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts. - Arturo Rodriguez; Ashesh Chattopadhyay; Piyush Kumar; Luis F. Rodriguez; Vinod Kumar (CoRR, 2024)

Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators. - Leonard Lupin-Jimenez; Moein Darman; Subhashis Hazarika; Tianning Wu; Michael Gray; Ruyoing He; Anthony Wong; Ashesh Chattopadhyay (CoRR, 2025)

Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence. - Moein Darman; Pedram Hassanzadeh; Laure Zanna; Ashesh Chattopadhyay (CoRR, 2025)

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity. - Niloofar Asefi; Leonard Lupin-Jimenez; Tianning Wu; Ruoying He; Ashesh Chattopadhyay (CoRR, 2025)