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Publications
2022
Cea, A., & Palacios, R. (2022). Parametric Reduced Order Models for the Aeroelastic Design of Flexible Vehicles . In AIAA SCITECH 2022 Forum (p. 0727). Cho, N., Shin, H. S., Tsourdos, A., & Amato, D. (2022). Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction . arXiv preprint arXiv:2209.03698 .
Diessner, M., O’Connor, J., Wynn, A., Laizet, S., Guan, Y., Wilson, K., & Whalley, R. D. (2022). Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in Fluid Dynamics Frontiers in Applied Mathematics and Statistics .
Fantuzzi, G., Arslan, A., & Wynn, A. (2022). The background method: theory and computations Philosophical Transactions of the Royal Society A , 380 (2225), 20210038.
Fasel, U., Kutz, J. N., Brunton, B. W., & Brunton, S. L. (2022). Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control Proceedings of the Royal Society A , 478 (2260), 20210904.
Giannakeas, I. N., Khodaei, Z. S., & Aliabadi, M. H. (2022). Digital clone testing platform for the assessment of SHM systems under uncertainty . Mechanical Systems and Signal Processing , 163 , 108150. Goizueta, N., Wynn, A., & Palacios, R. (2022). Adaptive Sampling for Interpolation of Reduced-Order Aeroelastic Systems . AIAA Journal , 1-20. He, X., Tan, J., Rigas, G., & Vahdati, M. (2022). On the explainability of machine-learning-assisted turbulence modeling for transonic flows . International Journal of Heat and Fluid Flow , 97 , 109038. Hickner, M. K., Fasel, U., Nair, A. G., Brunton, B. W., & Brunton, S. L. (2022). Data-driven unsteady aeroelastic modeling for control AIAA Journal , 1-14.
Ho, B., Kocer, B. B., & Kovac, M. (2022). Vision based crown loss estimation for individual trees with remote aerial robots . ISPRS Journal of Photogrammetry and Remote Sensing , 188 , 75-88. Huhn, F. & Magri, L. (2022), Gradient-free optimization of chaotic acoustics with reservoir computing , Physical review fluids, 7, 014402. Karmakov, S., & Aliabadi, M. F. (2022). Deep Learning Approach to Impact Classification in Sensorized Panels Using Self-Attention . Sensors , 22 (12), 4370. Kaheman, K., Fasel, U., Bramburger, J. J., Strom, B., Kutz, J. N., & Brunton, S. L. (2022). The experimental multi-arm pendulum on a cart: A benchmark system for chaos, learning, and control arXiv preprint arXiv:2205.06231 .
Lino, M., Fotiadis, S., Bharath, A. A., & Cantwell, C. (2022). Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks . arXiv preprint arXiv:2205.02637 . Lino, M., Fotiadis, S., Bharath, A. A., & Cantwell, C. D. (2022). Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics . Physics of Fluids , 34 (8), 087110. Nagy, P., Jones, B., Minisci, E., Fossati, M., Cea, A., Palacios, R., Roussouly, N. and Gazaix, A., (2022). Multi-fidelity nonlinear aeroelastic analysis of a strut-braced ultra-high aspect ratio wing configuration . In AIAA AVIATION 2022 Forum (p. 3668). Nortmann, B., & Mylvaganam, T. (2022, July). Data-driven cost representation for optimal control and its relevance to a class of asymmetric linear quadratic dynamic games. In 2022 European Control Conference (ECC) (pp. 2185-2190). IEEE.
Novoa, A. & Magri, L. (2022), Real-time thermoacoustic data assimilation , Journal of Fluid Mechanics, doi:10.1017/jfm.2022.653. Özbay, A. G., & Laizet, S. (2022). Deep learning fluid flow reconstruction around arbitrary two-dimensional objects from sparse sensors using conformal mappings . AIP Advances , 12 (4), 045126. Racca, A. & Magri, L. (2022), Data-driven prediction and control of extreme events in a chaotic flow , Physical Review Fluids, accepted for publication, to appear. Racca, A. & Magri, L. (2022), Statistical prediction of extreme events from small datasets , Lecture Notes on Computer Science, DOI: 10.1007/978-3-031-08757-8_58. Seno, A. H., & Aliabadi, M. F. (2022). Uncertainty quantification for impact location and force estimation in composite structures . Structural Health Monitoring , 21 (3), 1061-1075. Simiriotis, N., & Palacios, R. (2023). A numerical investigation on direct and data-driven flutter prediction methods Journal of Fluids and Structures , 117 , 103835.
Stephens, B., Orr, L., Kocer, B. B., Nguyen, H. N., & Kovac, M. (2022). An Aerial Parallel Manipulator with Shared Compliance . IEEE Robotics and Automation Letters . Valencia, M. L., Fotiadis, S., Bharath, A. A., & Cantwell, C. D. (2022, March). REMuS -GNN: A Rotation-Equivariant Model for Simulating Continuum Dynamics . In ICLR 2022 Workshop on Geometrical and Topological Representation Learning . Xuereb Conti, Z., Choudhary, R., Magri, L. (2022) A Physics-based Domain Adaptation framework for modelling and forecasting building energy systems , Data-centric engineering, under review. Zhuang, M., Morse, L., Khodaei, Z. S., & Aliabadi, M. H. (2022). Statistical inference of the Equivalent Initial Flaw Size Distribution for an anisotropic material with the Dual Boundary Element Method . International Journal of Fatigue , 158 , 106702.
2021
Amato, D., & McMahon, J. W. (2021). Deep learning method for Martian atmosphere reconstruction . Journal of Aerospace Information Systems , 18 (10), 728-738. Callaham, J. L., Loiseau, J. C., Rigas, G., & Brunton, S. L. (2021). Nonlinear stochastic modelling with Langevin regression . Proceedings of the Royal Society A , 477 (2250), 20210092. Doan, N. A. K., Polifke , W. & Magri, L. (2021), Short- and long-term prediction of chaotic flows and extreme events: A physics-constrained reservoir computing approach , Proceedings of the Royal Society A, 477:20210135. Doan, N. A. K., Polifke , W. & Magri, L. (2021), Auto-Encoded Reservoir Computing for Turbulence Learning , Lecture Notes in Computer Science book series (LNCS), vol 12746, 344-351 . He, X., Fang, Z., Rigas, G., & Vahdati, M. (2021). Spectral proper orthogonal decomposition of compressor tip leakage flow . Physics of Fluids , 33 (10), 105105. Kneer, S., Sayadi, T., Sipp, D., Schmid, P., & Rigas, G. (2021). Symmetry-Aware Autoencoders: s-PCA and s- nlPCA . arXiv preprint arXiv:2111.02893 . Kocer, B. B., Ho, B., Zhu, X., Zheng, P., Farinha, A., Xiao, F., ... & Kovac, M. (2021, October). Forest drones for environmental sensing and nature conservation . In 2021 Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO) (pp. 1-8). IEEE. Kocer, B. B., Hady, M. A., Kandath, H., Pratama, M., & Kovac, M. (2021, May). Deep neuromorphic controller with dynamic topology for aerial robots . In 2021 IEEE International Conference on Robotics and Automation (ICRA) (pp. 110-116). IEEE. Rigas, G. (2021). Control of Partially Observable Flows with Model-Free Reinforcement Learning . In APS Division of Fluid Dynamics Meeting Abstracts (pp. H23-006). Racca, A. & Magri, L. (2021), Automatic-differentiated Physics-Informed Echo State Network (API-ESN) , Lecture Notes in Computer Science book series (LNCS), vol 12746, 323-329 . Racca, A. & Magri, L. (2021), Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics , Neural Networks, Vol. 142, pp. 252–268 . Özbay, A. G., Hamzehloo, A., Laizet, S., Tzirakis, P., Rizos, G., & Schuller, B. (2021). Poisson CNN: Convolutional neural networks for the solution of the Poisson equation on a Cartesian mesh . Data-Centric Engineering , 2 . Yoo, K., Bacarreza, O., & Aliabadi, M. F. (2021). Multi-fidelity probabilistic optimisation of composite structures under thermomechanical loading using Gaussian processes . Computers & Structures , 257 , 106655. Yu, H., Juniper, M. P. & Magri, L. (2021), A data-driven kinematic model of a ducted premixed flame , Proceedings of the Combustion Institute, 38(4), pp. 6231–6239.
2020
Amato, D., Hume, S., Grace, B., & McMahon, J. (2020). Robustifying Mars descent guidance through neural networks . In Robustifying Mars Descent Guidance Through Neural Networks, number February, AAS Guidance, Navigation, and Control Conference . Dams, B., Sareh, S., Zhang, K., Shepherd, P., Kovac, M., & Ball, R. J. (2020). Aerial additive building manufacturing: three-dimensional printing of polymer structures using drones . Proceedings of the Institution of Civil Engineers-Construction Materials , 173 (1), 3-14. Doan, N. A. K., Polifke , W. & Magri, L. (2020), Physics-informed echo state networks , Journal of Computational Science, vol. 47, 101237. Lino, M., Cantwell, C., Fotiadis, S., Pignatelli, E., & Bharath, A. (2020). Simulating surface wave dynamics with convolutional networks . arXiv preprint arXiv:2012.00718 . Miriyev, A., & Kovač, M. (2020). Skills for physical artificial intelligence . Nature Machine Intelligence , 2 (11), 658-660. Salehzadeh Nobari, A. E., & Aliabadi, M. F. (2020). A multilevel isolation forrest and convolutional neural network algorithm for impact characterization on composite structures . Sensors , 20 (20), 5896.
2019
Doan, N. A. K., Polifke , W. & Magri, L. (2019), Physics-Informed Echo State Networks for Chaotic Systems Forecasting , Lecture Notes in Computer Science book series (LNCS), vol. 11539. 24. Seno, A. H., Khodaei, Z. S., & Aliabadi, M. F. (2019). Passive sensing method for impact localisation in composite plates under simulated environmental and operational conditions . Mechanical Systems and Signal Processing , 129 , 20-36. Tabian, I., Fu, H., & Sharif Khodaei, Z. (2019). A convolutional neural network for impact detection and characterization of complex composite structures . Sensors , 19 (22), 4933. Traverso, T. & Magri, L. (2019), Data assimilation in a nonlinear time-delayed dynamical system with Lagrangian optimization , Lecture Notes in Computer Science book series (LNCS), vol. 11539. Yu, H., Juniper, M. P. & Magri, L. (2019), Combined state and parameter estimation in level-set methods , Journal of Computational Physics, vol. 399, 108950. Yu, H., Jaravel , T., Juniper, M. P., Ihme, M. & Magri, L. (2019), Data assimilation and optimal calibration in nonlinear models of flame dynamics , J. Eng. Gas turbines and power, 141(12).
2018 2016
