Ashesh Chattopadhyay | DeepAI

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∙ 04/14/2023

Long-term instabilities of deep learning-based digital twins of the climate system: The cause and a solution

Long-term stability is a critical property for deep learning-based data-...

2 Ashesh Chattopadhyay, et al. ∙

∙ 06/09/2022

Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems

Data assimilation (DA) is a key component of many forecasting models in ...

32 Ashesh Chattopadhyay, et al. ∙

∙ 06/07/2022

Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow

Transfer learning (TL) is becoming a powerful tool in scientific applica...

8 Adam Subel, et al. ∙

∙ 05/09/2022

Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence

Recent years have seen a surge in interest in building deep learning-bas...

5 Ashesh Chattopadhyay, et al. ∙

∙ 02/22/2022

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

FourCastNet, short for Fourier Forecasting Neural Network, is a global d...

64 Jaideep Pathak, et al. ∙

∙ 10/01/2021

Closed-form discovery of structural errors in models of chaotic systems by integrating Bayesian sparse regression and data assimilation

Models used for many important engineering and natural systems are imper...

13 Rambod Mojgani, et al. ∙

∙ 03/16/2021

Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers

There is growing interest in data-driven weather prediction (DDWP), for ...

9 Ashesh Chattopadhyay, et al. ∙

∙ 02/25/2020

Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning

To make weather/climate modeling computationally affordable, small-scale...

14 Ashesh Chattopadhyay, et al. ∙

∙ 07/26/2019

Analog forecasting of extreme-causing weather patterns using deep learning

Numerical weather prediction (NWP) models require ever-growing computing...

0 Ashesh Chattopadhyay, et al. ∙

∙ 06/20/2019

Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM

In this paper, the performance of three deep learning methods for predic...

2 Ashesh Chattopadhyay, et al. ∙

∙ 11/12/2018

A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns

Convolutional neural networks (CNNs) can potentially provide powerful to...

4 Ashesh Chattopadhyay, et al. ∙