Zobrazeno 1 - 10
of 58
pro vyhledávání: '"P. Laiu"'
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 equation for gas dynamics. The MM formulation represents a kinetic distri
Externí odkaz:
http://arxiv.org/abs/2410.00678
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 iterations are performed between each AA step, referred to as the Alte
Externí odkaz:
http://arxiv.org/abs/2407.10472
Autor:
Schotthöfer, Steffen, Laiu, M. Paul
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 horizontal federated learning. Our method builds upon dynamical low-rank sp
Externí odkaz:
http://arxiv.org/abs/2406.17887
Autor:
J.D. Lore, S. De Pascuale, P. Laiu, B. Russo, J.-S. Park, J.M. Park, S.L. Brunton, J.N. Kutz, A.A. Kaptanoglu
Publikováno v:
Nuclear Fusion, Vol 63, Iss 4, p 046015 (2023)
Time-dependent SOLPS-ITER simulations have been used to identify reduced models with the sparse identification of nonlinear dynamics (SINDy) method and develop model-predictive control of the boundary plasma state using main ion gas puff actuation. A
Externí odkaz:
https://doaj.org/article/05fdcdcbb54848beb652a23e64aff3e4
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. In this work, we derive and investigate a neural network-based approxi
Externí odkaz:
http://arxiv.org/abs/2404.14312
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 particles interacting with a background fluid moving with nonrelativistic vel
Externí odkaz:
http://arxiv.org/abs/2309.04429
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 experiments, is undergoing an stage of exponential growth, which makes dat
Externí odkaz:
http://arxiv.org/abs/2308.14962
Autor:
Russo, Benjamin, Laiu, M. Paul
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 overview of a variety of non-linear system identification techniques, na
Externí odkaz:
http://arxiv.org/abs/2209.15573
We present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a dataset of Gibbs free energies computed from a CALPHAD database to pr
Externí odkaz:
http://arxiv.org/abs/2209.05609
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 approximate calculations satisfy the provided error bounds, the convergence of
Externí odkaz:
http://arxiv.org/abs/2206.03915