455 citations · 1.6k across the 28 of their papers we have counts for
28 papers
Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics
Conrad Ainslie, Pedram Hassanzadeh, Michael W. Mahoney +1
Neural autoregressive models have rapidly emerged as powerful emulators of high-dimensional chaotic systems, yet their long-term instability and error growth remain poorly understo…
SWR-Viz: AI-assisted Interactive Visual Analytics Framework for Ship Weather Routing
Subhashis Hazarika, Leonard Lupin-Jimenez, Rohit Vuppala +2
Efficient and sustainable maritime transport increasingly depends on reliable forecasting and adaptive routing, yet operational adoption remains difficult due to forecast latencies…
Deep learning the sources of MJO predictability: a spectral view of learned features
Lin Yao, Da Yang, James P. C. Duncan +4
The Madden-Julian oscillation (MJO) is a planetary-scale, intraseasonal tropical rainfall phenomenon crucial for global weather and climate; however, its dynamics and predictabilit…
Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
Niloofar Asefi, Leonard Lupin-Jimenez, Tianning Wu +2
Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and r…
Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence
Moein Darman, Pedram Hassanzadeh, Laure Zanna +1
Transfer learning (TL) is a powerful tool for enhancing the performance of neural networks (NNs) in applications such as weather and climate prediction and turbulence modeling. TL…
Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators
Leonard Lupin-Jimenez, Moein Darman, Subhashis Hazarika +5
Building on top of the success in AI-based atmospheric emulation, we propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean ov…
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 +2
Physics-informed neural networks (PINNs) commonly address ill-posed inverse problems by uncovering unknown physics. This study presents a novel unsupervised learning framework that…
What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning
Arvind Mohan, Ashesh Chattopadhyay, Jonah Miller
Differentiable Programming for scientific machine learning (SciML) has recently seen considerable interest and success, as it directly embeds neural networks inside PDEs, often cal…
Can AI weather models predict out-of-distribution gray swan tropical cyclones?
Y. Qiang Sun, Pedram Hassanzadeh, Mohsen Zand +3
Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI weather models and long-term climate…
Data Imbalance, Uncertainty Quantification, and Generalization via Transfer Learning in Data-driven Parameterizations: Lessons from the Emulation of Gravity Wave Momentum Transport in WACCM
Y. Qiang Sun, Hamid A. Pahlavan, Ashesh Chattopadhyay +6
Neural networks (NNs) are increasingly used for data-driven subgrid-scale parameterization in weather and climate models. While NNs are powerful tools for learning complex nonlinea…
OceanNet: A principled neural operator-based digital twin for regional oceans
Ashesh Chattopadhyay, Michael Gray, Tianning Wu +2
While data-driven approaches demonstrate great potential in atmospheric modeling and weather forecasting, ocean modeling poses distinct challenges due to complex bathymetry, land,…
Interpretable structural model error discovery from sparse assimilation increments using spectral bias-reduced neural networks: A quasi-geostrophic turbulence test case
Rambod Mojgani, Ashesh Chattopadhyay, Pedram Hassanzadeh
Earth system models suffer from various structural and parametric errors in their representation of nonlinear, multi-scale processes, leading to uncertainties in their long-term pr…
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
Karan Jakhar, Yifei Guan, Rambod Mojgani +2
There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we app…
Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution
Ashesh Chattopadhyay, Y. Qiang Sun, Pedram Hassanzadeh
Long-term stability and physical consistency are critical properties for AI-based weather models if they are going to be used for subseasonal-to-seasonal forecasts or beyond, e.g.,…
Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems
Ashesh Chattopadhyay, Ebrahim Nabizadeh, Eviatar Bach +1
Data assimilation (DA) is a key component of many forecasting models in science and engineering. DA allows one to estimate better initial conditions using an imperfect dynamical mo…
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow
Adam Subel, Yifei Guan, Ashesh Chattopadhyay +1
Transfer learning (TL) is becoming a powerful tool in scientific applications of neural networks (NNs), such as weather/climate prediction and turbulence modeling. TL enables out-o…
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence
Ashesh Chattopadhyay, Jaideep Pathak, Ebrahim Nabizadeh +2
Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models if trained on observations can…
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
Jaideep Pathak, Shashank Subramanian, Peter Harrington +10
FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.2…
Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES
Yifei Guan, Adam Subel, Ashesh Chattopadhyay +1
We demonstrate how incorporating physics constraints into convolutional neural networks (CNNs) enables learning subgrid-scale (SGS) closures for stable and accurate large-eddy simu…
Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test case
Rambod Mojgani, Ashesh Chattopadhyay, Pedram Hassanzadeh
Models of many engineering and natural systems are imperfect. The discrepancy between the mathematical representations of a true physical system and its imperfect model is called t…
Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh +2
There is growing interest in data-driven weather prediction (DDWP), for example using convolutional neural networks such as U-NETs that are trained on data from models or reanalysi…
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 +1
There is a growing interest in developing data-driven subgrid-scale (SGS) models for large-eddy simulation (LES) using machine learning (ML). In a priori (offline) tests, some rece…
Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning
Adam Subel, Ashesh Chattopadhyay, Yifei Guan +1
Developing data-driven subgrid-scale (SGS) models for large eddy simulations (LES) has received substantial attention recently. Despite some success, particularly in a priori (offl…
Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning
Ashesh Chattopadhyay, Adam Subel, Pedram Hassanzadeh
To make weather/climate modeling computationally affordable, small-scale processes are usually represented in terms of the large-scale, explicitly-resolved processes using physics-…