455 citations · 1.6k across the 28 of their papers we have counts for
7 papers · 1 filter
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…
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…
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,…
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…
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…
Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM
Ashesh Chattopadhyay, Pedram Hassanzadeh, Devika Subramanian
In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lore…