4 papers
In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization
Cooper Simpson, Stephen Becker, Alireza Doostan
Focusing on implicit neural representations, we present a novel in situ training protocol that employs limited memory buffers of full and sketched data samples, where the sketched…
Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search
Nuojin Cheng, Alireza Doostan, Stephen Becker
Efficient optimization remains a fundamental challenge across numerous scientific and engineering domains, especially when objective function and gradient evaluations are computati…
Langevin Bi-fidelity Importance Sampling for Failure Probability Estimation
Nuojin Cheng, Alireza Doostan
Estimating failure probability is a key task in the field of uncertainty quantification. In this domain, importance sampling has proven to be an effective estimation strategy; howe…
Online randomized interpolative decomposition with a posteriori error estimator for temporal PDE data reduction
Angran Li, Stephen Becker, Alireza Doostan
Traditional low-rank approximation is a powerful tool to compress the huge data matrices that arise in simulations of partial differential equations (PDE), but suffers from high co…