455 citations · 2.1k across the 56 of their papers we have counts for
10 papers · 1 filter
Designing probabilistic AI monsoon forecasts to inform agricultural decision-making
Colin Aitken, Rajat Masiwal, Adam Marchakitus +7
Hundreds of millions of farmers make high-stakes decisions under uncertainty about future weather. Forecasts can inform these decisions, but available choices and their risks and b…
Decision-oriented benchmarking to transform AI weather forecast access: Application to the Indian monsoon
Rajat Masiwal, Colin Aitken, Adam Marchakitus +9
Artificial intelligence weather prediction (AIWP) models now often outperform traditional physics-based models on common metrics while requiring orders-of-magnitude less computing…
Reframing Generative Models for Physical Systems using Stochastic Interpolants
Anthony Zhou, Alexander Wikner, Amaury Lancelin +2
Generative models have recently emerged as powerful surrogates for physical systems, demonstrating increased accuracy, stability, and/or statistical fidelity. Most approaches rely…
Hierarchical Implicit Neural Emulators
Ruoxi Jiang, Xiao Zhang, Karan Jakhar +4
Neural PDE solvers offer a powerful tool for modeling complex dynamical systems, but often struggle with error accumulation over long time horizons and maintaining stability and ph…
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…
Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence
Rambod Mojgani, Daniel Waelchli, Yifei Guan +2
Global climate models (GCMs) are the main tools for understanding and predicting climate change. However, due to limited numerical resolutions, these models suffer from major struc…
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…
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks
Rambod Mojgani, Maciej Balajewicz, Pedram Hassanzadeh
Physics-informed neural networks (PINNs) leverage neural-networks to find the solutions of partial differential equation (PDE)-constrained optimization problems with initial condit…
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…