12 papers
An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees
Hoang Tran, Jorge Ramirez, Jiayi Wang +3
Fine-tuning adapts a pretrained machine learning model to a small, sensitive dataset, but this process risks memorizing individual new data points, making the model vulnerable to a…
thornado+Flash-X: A Hybrid DG-IMEX and Finite-Volume Framework for Neutrino-Radiation Hydrodynamics in Core-Collapse Supernovae
Eirik Endeve, Vassilios Mewes, J. Austin Harris +15
We present neutrino-transport algorithms implemented in the toolkit for high-order neutrino-radiation hydrodynamics (thornado) and their coupling to self-gravitating hydrodynamics…
On high-order/low-order and micro-macro methods for implicit time-stepping of the BGK model
Cory Hauck, M. Paul Laiu, Stefan Schnake
In this paper, a high-order/low-order (HOLO) method is combined with a micro-macro (MM) decomposition to accelerate iterative solvers in fully implicit time-stepping of the BGK equ…
Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit
Junqi Yin, Mijanur Palash, M. Paul Laiu +6
Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essent…
Convergence Analysis of the Alternating Anderson-Picard Method for Nonlinear Fixed-point Problems
Xue Feng, M. Paul Laiu, Thomas Strohmer
Anderson Acceleration (AA) has been widely used to solve nonlinear fixed-point problems due to its rapid convergence. This work focuses on a variant of AA in which multiple Picard…
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
Xue Feng, M. Paul Laiu, Thomas Strohmer
Federated learning (FL) is a distributed machine learning approach that enables multiple local clients and a central server to collaboratively train a model while keeping the data…