1 citations · 1 across the 1 of their papers we have counts for
4 papers
PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations
Fabien Casenave, Xavier Roynard, Brian Staber +17
Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is limited by the lack of large-sca…
Asymmetric conformal prediction with penalized kernel sum-of-squares
Louis Allain, Sébastien Da Veiga, Brian Staber
Conformal prediction (CP) is a distribution-free method to construct reliable prediction intervals that has gained significant attention in recent years. Despite its success and va…
Scalable and adaptive prediction bands with kernel sum-of-squares
Louis Allain, Sébastien da Veiga, Brian Staber
Conformal Prediction (CP) is a popular framework for constructing prediction bands with valid coverage in finite samples, while being free of any distributional assumption. A well-…
Learning signals defined on graphs with optimal transport and Gaussian process regression
Raphaël Carpintero Perez, Sébastien da Veiga, Josselin Garnier +1
In computational physics, machine learning has now emerged as a powerful complementary tool to explore efficiently candidate designs in engineering studies. Outputs in such supervi…