56 citations · 177 across the 16 of their papers we have counts for
6 papers · 1 filter
SciAI4Industry -- Solving PDEs for industry-scale problems with deep learning
Philipp A. Witte, Russell J. Hewett, Kumar Saurabh +2
Solving partial differential equations with deep learning makes it possible to reduce simulation times by multiple orders of magnitude and unlock scientific methods that typically…
DeepG2P: Fusing Multi-Modal Data to Improve Crop Production
Swati Sharma, Aditi Partap, Maria Angels de Luis Balaguer +2
Agriculture is at the heart of the solution to achieve sustainability in feeding the world population, but advancing our understanding on how agricultural output responds to climat…
Causal Modeling of Soil Processes for Improved Generalization
Somya Sharma, Swati Sharma, Andy Neal +5
Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems. Soil organic carbon not only enriches nut…
Machine learning can guide experimental approaches for protein digestibility estimations
Sara Malvar, Anvita Bhagavathula, Maria Angels de Luis Balaguer +2
Food protein digestibility and bioavailability are critical aspects in addressing human nutritional demands, particularly when seeking sustainable alternatives to animal-based prot…
Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs
Thomas J. Grady, Rishi Khan, Mathias Louboutin +5
Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been show…
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations
Jinhyun So, Kevin Hsieh, Behnaz Arzani +3
Large-scale deployments of low Earth orbit (LEO) satellites collect massive amount of Earth imageries and sensor data, which can empower machine learning (ML) to address global cha…