2 citations · 2 across the 4 of their papers we have counts for
5 papers
Curating Grounded Synthetic Data with Global Perspectives for Equitable AI
Elin Törnquist, Robert Alexander Caulk
The development of robust AI models relies heavily on the quality and variety of training data available. In fields where data scarcity is prevalent, synthetic data generation offe…
High Throughput Training of Deep Surrogates from Large Ensemble Runs
Lucas Meyer, Marc Schouler, Robert Alexander Caulk +2
Recent years have seen a surge in deep learning approaches to accelerate numerical solvers, which provide faithful but computationally intensive simulations of the physical world.…
Balancing Computational Efficiency and Forecast Error in Machine Learning-based Time-Series Forecasting: Insights from Live Experiments on Meteorological Nowcasting
Elin Törnquist, Wagner Costa Santos, Timothy Pogue +2
Machine learning for time-series forecasting remains a key area of research. Despite successful application of many machine learning techniques, relating computational efficiency t…
Training Deep Surrogate Models with Large Scale Online Learning
Lucas Meyer, Marc Schouler, Robert Alexander Caulk +2
The spatiotemporal resolution of Partial Differential Equations (PDEs) plays important roles in the mathematical description of the world's physical phenomena. In general, scientis…
Yade Documentation
Vaclav Smilauer, Vasileios Angelidakis, Emanuele Catalano +24
Yade is an extensible open-source framework for discrete numerical models, focused on the Discrete Element Method. The computation parts are written in c++ using a flexible object…