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Dynamic substructuring (DS) methods encompass a range of techniques to decompose large structural systems into multiple coupled subsystems. This decomposition has the principle benefit of reducing computational time for dynamic simulation…

Computational Engineering, Finance, and Science · Computer Science 2020-07-01 Thomas Simpson , Dimitrios Giagopoulos , Vasilis Dertimanis , Eleni Chatzi

Consider a network of $N$ decentralized computing agents collaboratively solving a nonconvex stochastic composite problem. In this work, we propose a single-loop algorithm, called DEEPSTORM, that achieves optimal sample complexity for this…

Optimization and Control · Mathematics 2023-04-14 Gabriel Mancino-Ball , Shengnan Miao , Yangyang Xu , Jie Chen

This paper considers robust stability analysis of a large network of interconnected uncertain systems. To avoid analyzing the entire network as a single large, lumped system, we model the network interconnections with integral quadratic…

Optimization and Control · Mathematics 2016-11-17 Martin S. Andersen , Anders Hansson , Sina Khoshfetrat Pakazad , Anders Rantzer

We continue to develop a new approach to description of charge kinetics in disordered semiconductors. It is based on fractional diffusion equations. This article is devoted to transient processes in structures under dispersive transport…

Disordered Systems and Neural Networks · Physics 2013-10-02 Renat T. Sibatov , Vladimir V. Uchaikin

We develop a multi-dimensional, parallelized domain decomposition strategy (DDC) for mass-transfer particle tracking (MTPT) methods. These methods are a type of Lagrangian algorithm for simulating reactive transport and are able to be…

Periodic nonuniform sampling is a known method to sample spectrally sparse signals below the Nyquist rate. This strategy relies on the implicit assumption that the individual samplers are exposed to the entire frequency range. This…

Information Theory · Computer Science 2009-01-27 Moshe Mishali , Yonina C. Eldar , Joel A. Tropp

Linear systems with large differences between coefficients ("discontinuous coefficients") arise in many cases in which partial differential equations(PDEs) model physical phenomena involving heterogeneous media. The standard approach to…

Mathematical Software · Computer Science 2009-05-04 Dan Gordon , Rachel Gordon

Spatial designs for monitoring stream networks, especially ephemeral systems, are typically non-standard, `sparse' and can be very complex, reflecting the complexity of the ecosystem being monitored, the scale of the population, and the…

Methodology · Statistics 2008-09-12 Melissa J. Dobbie , Brent L. Henderson , Don L. Stevens

Convergence failure and slow convergence rate are among the biggest challenges with solving the system of non-linear equations numerically. While using strictly small time steps sizes and unconditionally stable fully implicit scheme…

Numerical Analysis · Mathematics 2024-12-20 Hanyu Li , Mary F. Wheeler

Power electronic devices such as HVDC and FACTS can be used to improve the damping of poorly damped inter-area modes in large power systems. This involves the use of wide-area feedback signals, which are transmitted via communication…

Systems and Control · Electrical Eng. & Systems 2022-11-17 K. K. Gajjar , Kaustav Dey , A. M. Kulkarni

Given a linear system in a real or complex domain, linear regression aims to recover the model parameters from a set of observations. Recent studies in compressive sensing have successfully shown that under certain conditions, a linear…

Statistics Theory · Mathematics 2016-11-15 Henrik Ohlsson , Allen Y. Yang , Roy Dong , S. Shankar Sastry

We propose a decentralized subspace algorithm for identification of large-scale, interconnected systems that are described by sparse (multi) banded state-space matrices. First, we prove that the state of a local subsystem can be…

Systems and Control · Computer Science 2014-02-17 Aleksandar Haber , Michel Verhaegen

We propose a variant of the classical conditional gradient method for sparse inverse problems with differentiable measurement models. Such models arise in many practical problems including superresolution, time-series modeling, and matrix…

Optimization and Control · Mathematics 2015-07-07 Nicholas Boyd , Geoffrey Schiebinger , Benjamin Recht

The main computational cost of algorithms for computing reduced-order models of parametric dynamical systems is in solving sequences of very large and sparse linear systems. We focus on efficiently solving these linear systems, arising…

Numerical Analysis · Mathematics 2018-09-19 Navneet Pratap Singh , Kapil Ahuja

A local approach to the time integration of PDEs by exponential methods is proposed, motivated by theoretical estimates by A.Iserles on the decay of off-diagonal terms in the exponentials of sparse matrices. An overlapping domain…

Numerical Analysis · Mathematics 2015-05-12 Luca Bonaventura

Domain decomposition methods are among the most efficient for solving sparse linear systems of equations. Their effectiveness relies on a judiciously chosen coarse space. Originally introduced and theoretically proved to be efficient for…

Numerical Analysis · Mathematics 2022-01-10 Hussam Al Daas , Pierre Jolivet , Tyrone Rees

We consider the problem of shaping the transient step response of nonlinear systems to satisfy a class of integral constraints. Such constraints are inherent in hybrid energy systems consisting of energy sources and storage elements. While…

Systems and Control · Electrical Eng. & Systems 2020-12-24 Farzad Aalipour , Tuhin Das

Reconstructing system-level behavior from silicon traces is a critical problem in post-silicon validation of System-on-Chip designs. Current industrial practice in this area is primarily manual, depending on collaborative insights of the…

Hardware Architecture · Computer Science 2020-05-07 Yuting Cao , Hao Zheng , Sandip Ray , Jin Yang

Dynamic Distribution Decomposition (DDD) was introduced in Taylor-King et. al. (PLOS Comp Biol, 2020) as a variation on Dynamic Mode Decomposition. In brief, by using basis functions over a continuous state space, DDD allows for the fitting…

Machine Learning · Computer Science 2020-06-12 Jake P. Taylor-King , Cristian Regep , Jyothish Soman , Flawnson Tong , Catalina Cangea , Charlie Roberts

Near-term large quantum computers are not able to operate as a single processing unit. It is therefore required to partition a quantum circuit into smaller parts, and then each part is executed on a small unit. This approach is known as…

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