455 citations · 519 across the 3 of their papers we have counts for
8 papers
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…
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-…
Spline-based Interface Modeling and Optimization (SIMO) for Surface Tension and Contact Angle Measurements
Karan Jakhar, Ashesh Chattopadhyay, Atul Thakur +1
Surface tension and contact angle measurements are fundamental characterization techniques relevant to thermal and fluidic applications. Drop shape analysis techniques for the meas…
Analog forecasting of extreme-causing weather patterns using deep learning
Ashesh Chattopadhyay, Ebrahim Nabizadeh, Pedram Hassanzadeh
Numerical weather prediction (NWP) models require ever-growing computing time/resources, but still, have difficulties with predicting weather extremes. Here we introduce a data-dri…