Mahindra Rautela

Mahindra Rautela

Postdoctoral Researcher

Articles = 25, Citations ~ 720, H-index = 13. You can find the articles on my Google Scholar profile

2026

  1. Rautela, M, et al. “Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading.” AI & PDE: ICLR 2026 Workshop on AI and Partial Differential Equations. (paper / arxiv / code)
  2. Rautela, M, et al. “PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion.” arXiv preprint arXiv:2603.04606 (2026). (paper / arxiv / code)

2025

  1. Rautela, M., Williams, A., Scheinker, A. (2025). Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal. Physical Review E, 111(2), 025307. (paper / arxiv / code / data / video)
  2. Rautela, M.S., Most, A., Mansingh, S., Love, B.C., Scheinker, A., Oyen, D., Debardeleben, N., Lawrence, E. and Biswas, A., 2025. Morph: Pde foundation models with arbitrary data modality. (paper / arxiv / code)

2024

  1. Rautela, M., Williams, A., Scheinker, A. (2024). A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators. Scientific Reports, 14(1), 18157. (paper / arxiv / code / data / video)

  2. Monaco, E., Rautela, M., Gopalakrishnan, S., and Ricci, F. (2024). Machine learning algorithms for delaminations detection on composites panels by wave propagation signals analysis: Review, experiences and results. Progress in Aerospace Sciences, 146, 100994. (paper / arxiv / code / data / video)

  3. Rautela, M., Williams, A., and Scheinker, A. (2024). CBOL-Tuner: Classifier-pruned Bayesian optimization to explore temporally structured latent spaces for particle accelerator tuning. Under Review. (arxiv)

  4. Rautela, M., Williams, A., and Scheinker, A. (2024, August). Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal. In 32nd Linear Accelerator Conference (LINAC). (paper / arxiv / data / code / video)

  5. Rautela, M., Williams, A., and Scheinker, A. (2024, May). Towards latent space evolution of spatiotemporal dynamics of six-dimensional phase space of charged particle beams. In 15th International Conference on Particle Accelerators (IPAC). (paper / arxiv / data / code / video)

  6. Rautela, M., Williams, A., and Scheinker, A. (2024, May). Accelerator system parameter estimation using variational autoencoded latent regression. In 15th International Conference on Particle Accelerators (IPAC). (paper / arxiv / data / code / video)

2023

  1. Rautela, M., Mirfarah, M., Silva, C. E., Dyke, S., Maghareh, A., and Gopalakrishnan, S. (2023). Real-time rapid leakage estimation for deep space habitats using exponentially-weighted adaptively-refined search. Acta Astronautica, 203, 385-391. (paper / arxiv / code / data / video)

  2. Rautela, M., Senthilnath, J., and Gopalakrishnan, S. (2023, December). Bayesian optimized physics-informed neural network for estimating wave propagation velocities (Accepted/In-press). (paper / arxiv / data / code / video)

2022

  1. Rautela, M., Senthilnath, J., Huber, A., and Gopalakrishnan, S. (2022). Towards deep generation of guided wave representations for composite materials. IEEE Transactions on Artificial Intelligence, 5(3), 1102-1109. (paper / arxiv / code / data / video)

  2. Rautela, M., Huber, A., Senthilnath, J., and Gopalakrishnan, S. (2022). Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion. Mechanics of Advanced Materials and Structures, 29(27), 6595-6611. (paper / arxiv / code / data / video)

  3. Rautela, M., Senthilnath, J., Monaco, E., and Gopalakrishnan, S. (2022). Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations. Composite Structures, 291, 115579. (paper / arxiv / code / data / video)

  4. Rautela, M., Maghareh, A., Dyke, S., and Gopalakrishnan, S. (2022, June). Deep generative models for unsupervised delamination detection using guided waves. In 8th World Conference on Structural Control and Monitoring. (paper / arxiv / data / code / video)

  5. Rautela, M., Raut, M., and Gopalakrishnan, S. (2022, March). Simulation of guided waves for structural health monitoring using physics-informed neural networks. In International Workshop of Structural Health Monitoring (IWSHM). (paper / arxiv / data / code / video)

2021

  1. Rautela, M., Senthilnath, J., Moll, J., and Gopalakrishnan, S. (2021). Combined two-level damage identification strategy using ultrasonic guided waves and physical knowledge assisted machine learning. Ultrasonics, 115, 106451. (paper / arxiv / code / data / video)

  2. Rautela, M. and Gopalakrishnan, S. (2021). Ultrasonic guided wave based structural damage detection and localization using model assisted convolutional and recurrent neural networks. Expert Systems with Applications, 167, 114189. (paper / arxiv / code / data / video)

  3. Rautela, M., Monaco, E., and Gopalakrishnan, S. (2021, March). Delamination detection in aerospace composite panels using convolutional autoencoders. In Health Monitoring of Structural and Biological Systems XV (Vol. 11593, pp. 292-301). SPIE. (paper / arxiv / data / code / video)

  4. Rautela, M., Jayavelu, S., Moll, J., and Gopalakrishnan, S. (2021, March). Temperature compensation for guided waves using convolutional denoising autoencoders. In Health Monitoring of Structural and Biological Systems XV (Vol. 11593, pp. 316-326). SPIE. (paper / arxiv / data / code / video)

  5. Monaco, E., Boffa, N. D., Ricci, F., Rautela, M., Passato, D., and Cinque, M. (2021, March). Simulation of waves propagation into composites thin shells by FEM methodologies for training of deep neural networks aimed at damage reconstruction. In Health Monitoring of Structural and Biological Systems XV (Vol. 11593, pp. 302-315). SPIE. (paper / arxiv / data / code / video)

2020

  1. Gopalakrishnan, K., Rautela, M., and Deng, Y. (2020, July). Deep learning based identification of elastic properties using ultrasonic guided waves. In European Workshop on Structural Health Monitoring (pp. 77-90). Cham: Springer International Publishing. (paper / arxiv / data / code / video)

  2. Rautela, M., Gopalakrishnan, S., Gopalakrishnan, K., and Deng, Y. (2020, June). Ultrasonic guided waves based identification of elastic properties using 1D-convolutional neural networks. In 2020 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 1-7). IEEE. (paper / arxiv / data / code / video)

2019

  1. Rautela, M. and Bijudas, C. R. (2019). Electromechanical admittance based integrated health monitoring of adhesive bonded beams using surface bonded piezoelectric transducers. International Journal of Adhesion and Adhesives, 94, 84-98. (paper / arxiv / code / data / video)

  2. Rautela, M. and Gopalakrishnan, S. (2019, November). Deep learning frameworks for wave propagation-based damage detection in 1D-waveguides. In Proceedings of 11th International Symposium on NDT in Aerospace (Vol. 2, pp. 1-11). (paper / arxiv / data / code / video)