Publications

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 

• Amato, D., Furfaro, R., Rosengren, A. J., & Maadani, M. (2018, September). Attitude propagation of resident space objects with recurrent neural networks. In 2108 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS).

2016 

• Yue, N., & Sharif Khodaei, Z. (2016). Assessment of impact detection techniques for aeronautical application: ANN vs. LSSVM. Journal of Multiscale Modelling, 7(04), 1640005.