18 citations · 28 across the 16 of their papers we have counts for
17 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…
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…
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…
Realizability-Preserving Discontinuous Galerkin Method for Spectral Two-Moment Radiation Transport in Special Relativity
Joseph Hunter, Eirik Endeve, M. Paul Laiu +1
We present a realizability-preserving numerical method for solving a spectral two-moment model to simulate the transport of massless, neutral particles interacting with a steady ba…
MATEY: multiscale adaptive foundation models for spatiotemporal physical systems
Pei Zhang, M. Paul Laiu, Matthew Norman +2
Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer (ViT) architectures requires extremely long, computationally prohibit…
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…
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…
Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees
Steffen Schotthöfer, M. Paul Laiu
In this work, we propose a federated dynamical low-rank training (FeDLRT) scheme to reduce client compute and communication costs - two significant performance bottlenecks in horiz…
Structure-preserving neural networks for the regularized entropy-based closure of the Boltzmann moment system
Steffen Schotthöfer, M. Paul Laiu, Martin Frank +1
The main challenge of large-scale numerical simulation of radiation transport is the high memory and computation time requirements of discretization methods for kinetic equations.…
DG-IMEX Method for a Two-Moment Model for Radiation Transport in the Limit
M. Paul Laiu, Eirik Endeve, J. Austin Harris +2
We consider particle systems described by moments of a phase-space density and propose a realizability-preserving numerical method to evolve a spectral two-moment model for particl…
Streaming Compression of Scientific Data via weak-SINDy
Benjamin P. Russo, M. Paul Laiu, Richard Archibald
In this paper a streaming weak-SINDy algorithm is developed specifically for compressing streaming scientific data. The production of scientific data, either via simulation or expe…
Convergence of weak-SINDy Surrogate Models
Benjamin Russo, M. Paul Laiu
In this paper, we give an in-depth error analysis for surrogate models generated by a variant of the Sparse Identification of Nonlinear Dynamics (SINDy) method. We start with an ov…
Anderson acceleration with approximate calculations: applications to scientific computing
Massimiliano Lupo Pasini, M. Paul Laiu
We provide rigorous theoretical bounds for Anderson acceleration (AA) that allow for approximate calculations when applied to solve linear problems. We show that, when the approxim…
A DG-IMEX method for two-moment neutrino transport: Nonlinear solvers for neutrino-matter coupling
M. Paul Laiu, Eirik Endeve, Ran Chu +2
Neutrino-matter interactions play an important role in core-collapse supernova (CCSN) explosions as they contribute to both lepton number and/or four-momentum exchange between neut…
An Infeasible-Start Framework for Convex Quadratic Optimization, with Application to Constraint-Reduced Interior-Point Methods
M. Paul Laiu, André L. Tits
A framework is proposed for solving general convex quadratic programs (CQPs) from an infeasible starting point by invoking an existing feasible-start algorithm tailored for inequal…
Analysis of the Zero Relaxation Limit of Systems of Hyperbolic Conservation Laws with Random Initial Data
James M. Scott, M. Paul Laiu, Cory D. Hauck
We show the convergence of the zero relaxation limit in systems of hyperbolic conservation laws with stochastic initial data. Precisely, solutions converge to a soluti…