Results 81 to 90 of about 1,899,215 (187)

Reduced-order modeling and analysis of dynamic cerebral autoregulation via diffusion maps. [PDF]

Physiol Meas, 2023
Dos Santos KRM, Katsidoniotaki MI, Miller EC, Petersen NH, Marshall RS, Kougioumtzoglou IA.   +5 more
europepmc   +1 more source

Multi-fidelity reduced-order surrogate modelling

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations considered and/or time window evaluated. Multi-fidelity surrogate modelling aims to leverage less accurate, lower-fidelity models that are computationally ...
Paolo Conti, Mengwu Guo, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz   +5 more
openaire   +5 more sources

Vanquishing the computational cost of passive gamma emission tomography simulations leveraging physics-aware reduced order modeling. [PDF]

Sci Rep, 2023
Cavallini N, Ferretti R, Bostrom G, Croft S, Fassi A, Mercurio G, Nonneman S, Favalli A.   +7 more
europepmc   +1 more source

Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches. [PDF]

Sensors (Basel), 2023
Gobat G, Fresca S, Manzoni A, Frangi A.
europepmc   +1 more source

Efficient Dynamics: Reduced‐Order Modeling of the Time‐Dependent Schrödinger Equation

Advanced Physics Research
This work develops and rigorously analyzes reduced‐order modeling (ROM) techniques for the time‐dependent Schrödinger equation (TDSE), with the goal of efficiently capturing essential quantum dynamics at significantly reduced computational cost.
Kolade M. Owolabi
doaj   +1 more source

Reduced-Order Modeling of Parametrically Excited Micro-Electro-Mechanical Systems (MEMS)

Advances in Mechanical Engineering, 2010
Reduced-order modeling is a systematic way of constructing models with smaller number of states that can capture the “essential dynamics” of the large-scale systems, accurately.
Sangram Redkar
doaj   +1 more source

Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning. [PDF]

Sci Rep, 2022
Kadeethum T, Ballarin F, O'Malley D, Choi Y, Bouklas N, Yoon H.   +5 more
europepmc   +1 more source

Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders. [PDF]

Adv Model Simul Eng Sci, 2023
Abdedou A, Soulaimani A.
europepmc   +1 more source

Reduced order modeling of blood perfusion in parametric multipatch liver lobules

Advanced Modeling and Simulation in Engineering Sciences
In this paper, we present a computationally efficient reduced order model for obtaining blood perfusion profiles within parametric functional units of the liver called ‘lobules’.
Ahsan Ali Siddiqui, Etienne Jessen, Stein K. F. Stoter, David Néron, Dominik Schillinger   +4 more
doaj   +1 more source

A novel computational method to predict hypoattenuated leaflet thickening post-transcatheter aortic valve replacement using preprocedural computed tomography scansCentral MessagePerspective

JTCVS Structural and Endovascular
Objective: Hypoattenuated leaflet thickening (HALT) is a computed tomography (CT) finding after transcatheter aortic valve replacement (TAVR) that is indicative of bioprosthetic valvular thrombosis.
Aniket Venkatesh, MS, Fateme Esmailie, PhD, Noah Tregobov, BSc, Hoda Hatoum, PhD, Breandan Yeats, PhD, Huang Chen, PhD, Beom Jun Lee, MS, Philipp Ruile, MD, Franz-Josef Neumann, MD, Philipp Blanke, MD, Jonathon Leipsic, MD, Gaurav Gulsin, MD, Vinod Thourani, MD, David Meier, MD, Lakshmi Prasad Dasi, PhD, Stephanie L. Sellers, PhD   +15 more
doaj   +1 more source