Publications

(2023). Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(12): 12312..

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(2023). Efficient approximation of cardiac mechanics through reduced order modeling with deep learning-based operator approximation. International Journal for Numerical Methods in Biomedical Engineering, e3783.

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(2023). Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression. Computers and Mathematics with Applications, 149, 1-23.

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(2023). Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models. Mathematics in Engineering, 5(6):1-36.

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(2023). Modeling the periodic response of Micro-Electromechanical Systems through deep learning-based approaches. Actuators, 12, 278.

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(2023). Approximation bounds for convolutional neural networks in operator learning. Neural Networks, 161, 129-141.

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(2023). Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions. Computer Methods in Applied Mechanics and Engineering, 411, 116072.

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(2023). Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition. arXiv preprint arXiv:2305.04680.

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(2023). Reduced order modelling of nonlinear vibrating multiphysics microstructures with deep learning-based approaches. Sensors, 23(6), 3001.

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(2022). Neural latent dynamics models. 35th Conference on Neural Information Processing Systems (NeurIPS), The Symbiosis of Deep Learning and Differential Equations.

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(2022). Deep-HyROMnet: A deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs. Journal of Scientific Computing, 93:57.

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(2022). Deep learning-based reduced order models in cardiac electrophysiology. 7th International Conference on Computational and Mathematical Biomedical Engineering.

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(2022). Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures. International Journal for Numerical Methods in Engineering, 123(20):4749-4777.

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(2022). Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition. Mechanical Systems and Signal Processing, 171, 108864.

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(2022). Projection-based reduced order models for parameterized nonlinear time-dependent problems arising in cardiac mechanics. Mathematics in Engineering, 5(2):1-38.

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(2022). POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition. Computer Methods in Applied Mechanics and Engineering, 388, 114181.

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(2021). Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based ROMs. 35th Conference on Neural Information Processing Systems (NeurIPS), The Symbiosis of Deep Learning and Differential Equations.

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(2021). POD-enhanced deep learning-based reduced order models for the real-time simulation of cardiac electrophysiology in the left atrium. Frontiers in Physiology, 12, 1431.

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(2021). A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs. Journal of Scientific Computing, 87(2):1-36.

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(2020). Deep learning-based reduced order models in cardiac electrophysiology. PLOS ONE, 15(10):1-32.

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