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Reduced-order modeling of soft robots. [PDF]
PLoS ONE, 2018 We present a general strategy for the modeling and simulation-based control of soft robots. Although the presented methodology is completely general, we restrict ourselves to the analysis of a model robot made of hyperelastic materials and actuated by ...
Jean Chenevier +4 more
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REDUCED-ORDER MODELLING OF PARAMETERIZED TRANSIENT FLOWS IN CLOSED-LOOP SYSTEMS [PDF]
EPJ Web of Conferences, 2021 In this paper, two Galerkin projection based reduced basis approaches are investigated for the reduced-order modeling of parameterized incompressible Navier-Stokes equations for laminar transient flows. The first approach solves only the reduced momentum
German Péter +3 more
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Reduced Order Modeling Using Advection-Aware Autoencoders
Mathematical and Computational Applications, 2022 Physical systems governed by advection-dominated partial differential equations (PDEs) are found in applications ranging from engineering design to weather forecasting.
Sourav Dutta +3 more
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Component-Based Reduced Order Modeling of Large-Scale Complex Systems
Frontiers in Physics, 2022 Large-scale engineering systems, such as propulsive engines, ship structures, and wind farms, feature complex, multi-scale interactions between multiple physical phenomena.
Cheng Huang +2 more
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A Method to Minimize the Effort for Damper–Blade Matching Demonstrated on Two Blade Sizes
Applied Sciences, 2021 A method called PCR (Platform Centered Reduction) is designed to more effectively perform complex iterative and nonlinear calculations required for the dynamic response of turbine blades damped by dry friction contacts between rigid dampers and airfoil ...
Chiara Gastaldi, Muzio M. Gola
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Reduced order modeling and model order reduction for continuum manipulators: an overview
Frontiers in Robotics and AI, 2023 Soft robot’s natural dynamics calls for the development of tailored modeling techniques for control. However, the high-dimensional configuration space of the geometrically exact modeling approaches for soft robots, i.e., Cosserat rod and Finite Element ...
S.M.H. Sadati +4 more
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A Bayesian Nonlinear Reduced Order Modeling Using Variational AutoEncoders
Fluids, 2022 This paper presents a new nonlinear projection based model reduction using convolutional Variational AutoEncoders (VAEs). This framework is applied on transient incompressible flows.
Nissrine Akkari +3 more
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Lagrangian Reduced Order Modeling Using Finite Time Lyapunov Exponents
Fluids, 2020 There are two main strategies for improving the projection-based reduced order model (ROM) accuracy—(i) improving the ROM, that is, adding new terms to the standard ROM; and (ii) improving the ROM basis, that is, constructing ROM bases that yield more ...
Xuping Xie +4 more
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Data-driven reduced order modeling for mechanical oscillators using Koopman approaches
Frontiers in Applied Mathematics and Statistics, 2023 Data-driven reduced order modeling methods that aim at extracting physically meaningful governing equations directly from measurement data are facing a growing interest in recent years. The HAVOK-algorithm is a Koopman-based method that distills a forced,
Charlotte Geier +4 more
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Reduced order modeling of fluid flows using convolutional neural networks
Journal of Fluid Science and Technology, 2023 Application of machine learning is currently one of the hottest topics in the fluid mechanics field. While machine learning seems to have a great possibility, its limitations should also be clarified.
Koji FUKAGATA
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