| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Mukhopadhyay, Tanmoy
University of Southampton
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (43/43 displayed)
- 2024Nonlinear stability of curved multi-phase composite panels: influence of agglomeration in randomly distributed carbon nanotubes with non-uniform in-plane loadscitations
- 2023Programmable multi-physical mechanics of mechanical metamaterialscitations
- 2023Manipulating flexural waves to enhance the broadband vibration mitigation through inducing programmed disorder on smart rainbow metamaterialscitations
- 2023Multilevel fully integrated electromechanical property modulation of functionally graded graphene‐reinforced piezoelectric actuators: coupled effect of poling orientationcitations
- 2023On characterizing the viscoelastic electromechanical responses of functionally graded graphene-reinforced piezoelectric laminated compositescitations
- 2023Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer compositescitations
- 2023Viscoelastic free vibration analysis of in-plane functionally graded orthotropic plates integrated with piezoelectric sensors: Time-dependent 3D analytical solutionscitations
- 2023Programmed Out-of-Plane curvature to enhance multimodal stiffness of bending-dominated composite latticescitations
- 2023Effective elastic moduli of space-filled multi-material composite latticescitations
- 2023Probing the molecular-level energy absorption mechanism and strategic sequencing of graphene/Al composite laminates under high-velocity ballistic impact of nano-projectilescitations
- 2023Multi-fidelity machine learning based uncertainty quantification of progressive damage in composite laminates through optimal data fusioncitations
- 2022Voltage modulation of elastic properties of asymmetric hybrid lattice structurecitations
- 2022Unfolding the mechanical properties of buckypaper composites: nano- to macro-scale coupled atomistic-continuum simulationscitations
- 2022Modified embedded-atom method interatomic potentials for Al-Cu, Al-Fe and Al-Ni binary alloys: from room temperature to melting pointcitations
- 2022Damage modeling of MWCNT reinforced Carbon/Epoxy composite using different failure criteria: a comparative studycitations
- 2022Damage modeling of MWCNT reinforced Carbon/Epoxy composite using different failure criteria: a comparative studycitations
- 2022High-velocity ballistics of twisted bilayer graphene under stochastic disorder
- 2022Multiscale bending and free vibration analyses of functionally graded graphene platelet/fiber composite beams
- 2022Liquid ordering induced heterogeneities in homogeneous nucleation during solidification of pure metalscitations
- 2022Liquid ordering induced heterogeneities in homogeneous nucleation during solidification of pure metalscitations
- 2022Experimental data-driven uncertainty quantification for the dynamic fracture toughness of particulate polymer compositescitations
- 2022Probability-based unified sensitivity analysis for multi-objective performances of composite laminates: a surrogate-assisted approachcitations
- 2021Broadband dynamic elastic moduli of honeycomb lattice materials: a generalized analytical approachcitations
- 2021Voltage-dependent modulation of elastic moduli in lattice metamaterialscitations
- 2020Genetic programming-assisted multi-scale optimization for multi-objective dynamic performance of laminated composites: the advantage of more elementary-level analysescitations
- 2019Negative In-plane Elastic Moduli of Metallic Lattices: Experimental Investigations
- 2019Probing the frequency-dependent elastic moduli of lattice materialscitations
- 2019Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced compositescitations
- 2019Frequency domain homogenization for the viscoelastic properties of spatially correlated quasi-periodic latticescitations
- 2018Spatial vulnerability analysis for the first ply failure strength of composite laminates including effect of delaminationcitations
- 2018Probing the shear modulus of two-dimensional multiplanar nanostructures and heterostructurescitations
- 2018Probing the chirality-dependent elastic properties and crack propagation behavior of single and bilayer stanenecitations
- 2017Stochastic mechanics of metamaterialscitations
- 2017Metamodel based high-fidelity stochastic analysis of composite laminatescitations
- 2016Free-vibration analysis of sandwich panels with randomly irregular honeycomb corecitations
- 2016Effect of platen restraint on stress–strain behavior of concrete under uniaxial compressioncitations
- 2016Fuzzy uncertainty propagation in composites using Gram-Schmidt polynomial chaos expansioncitations
- 2016Probabilistic analysis and design of HCP nanowirescitations
- 2016Effective in-plane elastic properties of auxetic honeycombs with spatial irregularitycitations
- 2016Equivalent in-plane elastic properties of irregular honeycombscitations
- 2016Bottom up surrogate based approach for stochastic frequency response analysis of laminated composite platescitations
- 2015Stochastic natural frequency of composite conical shellscitations
- 2015Stochastic free vibration analysis of angle-ply composite plates - A RS-HDMR approachcitations
Places of action
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article
Multi-fidelity machine learning based uncertainty quantification of progressive damage in composite laminates through optimal data fusion
Abstract
Recently machine learning (ML) based approaches have gained significant attention in dealing with computationally intensive analyses such as uncertainty quantification of composite laminates. However, high-fidelity ML model construction is computationally demanding for such high-dimensional problems due to the required large amount of high-fidelity training data. We propose to address this issue effectively through multi-fidelity ML based surrogates which can use a training dataset consisting of optimally distributed high- and low-fidelity simulations. For forming multi-fidelity surrogates of progressive damage in composite laminates, we combine low-fidelity finite element analysis data obtained using Matzenmiller damage model with Hasin failure criteria and high-fidelity finite element analysis data obtained using three-dimensional continuum damage mechanics based model with P Linde's failure criteria. It is shown that there is a significant computational advantage to using the multi-fidelity surrogate approach as compared to conventional single-fidelity surrogates. Such computational advantage through optimal data fusion without compromising accuracy becomes crucial for the subsequent data-driven uncertainty quantification and sensitivity analysis of composites involving thousands of realizations. Ply orientations come out to be the most sensitive parameters to matrix damage, fibre damage and reaction force in composite laminates. The degree of uncertainty in the output quantities depend on the input-level stochastic variations. For example, a combined stochastic variation of ±10% in material properties and ±10° in ply orientations lead to 1.85%, 16.98% and 11.24% coefficient of variation in the matrix damage, fibre damage and reaction force respectively. In general, the numerical results obtained based on the efficient data-driven approach strongly suggest that source-uncertainty of composites significantly influences the progressive damage evolution and global mechanical behaviour, leading to the realization ...