1.7k citations · 2.1k across the 41 of their papers we have counts for
11 papers · 1 filter
Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift
Shuochen Wang, Nishant Yadav, Auroop R. Ganguly
Deep-learning-based climate downscaling aims to learn relationships from historical low-resolution (LR) and high-resolution (HR) climate data to generate HR climate projections. Ho…
Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping
August Posch, Jitendra Kumar, Forrest M. Hoffman +1
In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops. Currently, the USDA Cropland Data Layer provides cro…
Learning Transferable Predictability Representations
Diyali Goswami, Auroop R. Ganguly
We study the problem of assigning a scalar score to a short trajectory window that reflects its position on an ordered continuum of predictability regimes, spanning structured dete…
MAUNet-Light: A Concise MAUNet Architecture for Bias Correction and Downscaling of Precipitation Estimates
Sumanta Chandra Mishra Sharma, Adway Mitra, Auroop Ratan Ganguly
Satellite-derived data products and climate model simulations of geophysical variables like precipitation, often exhibit systematic biases compared to in-situ measurements. Bias co…
Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers
Shuochen Wang, Nishant Yadav, Auroop R. Ganguly
One of the major sources of uncertainty in the current generation of Global Climate Models (GCMs) is the representation of sub-grid scale physical processes. Over the years, a seri…
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance
Ted Fujimoto, Joshua Suetterlein, Samrat Chatterjee +1
Research in machine learning is making progress in fixing its own reproducibility crisis. Reinforcement learning (RL), in particular, faces its own set of unique challenges. Compar…
Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise
Nidhin Harilal, Udit Bhatia, Auroop R. Ganguly
Advances in neural architecture search, as well as explainability and interpretability of connectionist architectures, have been reported in the recent literature. However, our und…
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling
Nishant Yadav, Sai Ravela, Auroop R. Ganguly
Systems exhibiting nonlinear dynamics, including but not limited to chaos, are ubiquitous across Earth Sciences such as Meteorology, Hydrology, Climate and Ecology, as well as Biol…
A framework for deep learning emulation of numerical models with a case study in satellite remote sensing
Kate Duffy, Thomas Vandal, Weile Wang +2
Numerical models based on physics represent the state-of-the-art in earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth…
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Thomas Vandal, Evan Kodra, Jennifer Dy +3
Deep Learning (DL) methods have been transforming computer vision with innovative adaptations to other domains including climate change. For DL to pervade Science and Engineering (…
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
Anuj Karpatne, Gowtham Atluri, James Faghmous +6
Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guide…