An Efficient Simulation Method for Large-Scale Systems with Local Nonlinearities

Chen, Yousheng; Linderholt, Andreas; Abrahamsson, Thomas

doi:10.1007/978-3-319-29910-5_27

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Next, the main parameters of the bouncing ball are estimated from experimental data using a deep learning process to obtain an equivalent virtual simulation configuration. Previously, this model has been tested using discretization techniques and traditional computational optimizations with promising results [ 42 ]. The training process considers the energy loss and elastic energy from experimental data to obtain a closed-loop simulation.…”

Section: Resultsmentioning

confidence: 99%

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Predictive Modeling of Soft Stretchable Nanocomposites Using Recurrent Neural Networks

et al. 2022

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Human skin is characterized by rough, elastic, and uneven features that are difficult to recreate using conventional manufacturing technologies and rigid materials. The use of soft materials is a promising alternative to produce devices that mimic the tactile capabilities of biological tissues. Although previous studies have revealed the potential of fillers to modify the properties of composite materials, there is still a gap in modeling the conductivity and mechanical properties of these types of materials. While traditional Finite Element approximations can be used, these methodologies tend to be highly demanding of time and processing power. Instead of this approach, a data-driven learning-based approximation strategy can be used to generate prediction models via neural networks. This paper explores the fabrication of flexible nanocomposites using polydimethylsiloxane (PDMS) with different single-walled carbon nanotubes (SWCNTs) loadings (0.5, 1, and 1.5 wt.%). Simple Recurrent Neural Networks (SRNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) models were formulated, trained, and tested to obtain the predictive sequence data of out-of-plane quasistatic mechanical tests. Finally, the model learned is applied to a dynamic system using the Kelvin–Voight model and the phenomenon known as the bouncing ball. The best predictive results were achieved using a nonlinear activation function in the SRNN model implementing two units and 4000 epochs. These results suggest the feasibility of a hybrid approach of analogy-based learning and data-driven learning for the design and computational analysis of soft and stretchable nanocomposite materials.

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Section: Resultsmentioning

confidence: 99%

“… ( a ). Schematic representation of the deformation mechanism of bounding stretchable ball (a cross-section of the ball is shown); ( b ) A mass-spring-damper model of a bouncing ball showing phases of the first cycle (figure adapted and licensed from [ 42 ]). …”

Section: Figurementioning

confidence: 99%

Predictive Modeling of Soft Stretchable Nanocomposites Using Recurrent Neural Networks

et al. 2022

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“…In this example, the structure has a known story mass of 1.25 × 10 5 kg and a known earthquake ground acceleration defining the excitation u(t). Similar to [24], the east-west component of the Sylmar ground- In this study, we use the fixed-point exponential integrator [57] together with the particular numerical implementation of the two extended Masing rules that was proposed in [58] to simulate samples of the response time history y 1:N for given values of the uncertain parameters and the Sylmar ground-motion record.…”

Section: Example 2: Three-story Masing Shear-building Under Seismic Ementioning

confidence: 99%

Using Approximate Bayesian Computation by Subset Simulation for Efficient Posterior Assessment of Dynamic State-Space Model Classes

Vakilzadeh¹,

Beck²,

Abrahamsson³

2018

SIAM J. Sci. Comput.

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Approximate Bayesian Computation (ABC) methods have gained in their popularity over the last decade because they expand the horizon of Bayesian parameter inference methods to the range of models for which only forward simulation is available. The majority of the ABC methods rely on the choice of a set of summary statistics to reduce the dimension of the data. However, as has been noted in the ABC literature, the lack of convergence guarantees that is induced by the absence of a vector of sufficient summary statistics that assures inter-model sufficiency over the set of competing models, hinders the use of the usual ABC methods when applied to Bayesian model selection or assessment. In this paper, we present a novel ABC model selection procedure for dynamical systems based on a newly appeared multi-level Markov chain Monte Carlo method, self-regulating ABC-SubSim, and a hierarchical state-space formulation of dynamic models. We show that this formulation makes it possible to independently approximate the model evidence required for assessing the posterior probability of each of the competing models. We also show that ABC-SubSim not only provides an estimate of the model evidence as a simple by-product but also it gives the posterior probability of each model as a function of the tolerance level, which allows the ABC model choices made in previous studies to be understood. We illustrate the performance of the proposed framework for ABC model updating and model class selection by applying it to two problems in Bayesian system identification: a single degree-offreedom bilinear hysteretic oscillator and a three-story shear building with Masing hysteresis, both of which are subject to a seismic excitation.

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An Efficient Simulation Method for Large-Scale Systems with Local Nonlinearities

Cited by 2 publications

References 6 publications

Predictive Modeling of Soft Stretchable Nanocomposites Using Recurrent Neural Networks

Predictive Modeling of Soft Stretchable Nanocomposites Using Recurrent Neural Networks

Using Approximate Bayesian Computation by Subset Simulation for Efficient Posterior Assessment of Dynamic State-Space Model Classes

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