Arvind T. Mohan

Research record

Publications, Talks & Contact

Selected publications across trustworthy scientific AI, sparse sensing, reduced-order modeling, turbulence, earth systems, nuclear physics, and biomedical modeling.

For the complete, always up-to-date list, visit Google Scholar.

Selected publications

  1. Attention-based reconstruction of full-field tsunami waves from sparse tsunameter networks. Geophysical Research Letters 52 (2025).
  2. Reduced Order Modeling for Tsunami Forecasting with Bayesian Hierarchical Pooling. arXiv preprint (2025).
  3. Physics-constrained coupled neural differential equations for one-dimensional blood flow modeling. Computers in Biology and Medicine (2025).
  4. Development of the Senseiver for efficient field reconstruction from sparse observations. Nature Machine Intelligence 5 (2023).
  5. Embedding hard physical constraints in neural network coarse-graining of 3D turbulence. Physical Review Fluids 8 (2023).
  6. Machine learning technique for isotopic determination of radioisotopes using HPGe γ-ray spectra. Nuclear Instruments and Methods in Physics Research Section A (2023).
  7. Physically interpretable machine learning for nuclear masses. Physical Review C 106 (2022).
  8. Nuclear masses learned from a probabilistic neural network. Physical Review C 106 (2022).
  9. Validation and parameterization of a physics-constrained neural dynamics model for turbulent flow. Physics of Fluids 34 (2022).
  10. Spatio-temporal deep learning models of 3D turbulence with physics-informed diagnostics. Journal of Turbulence 21 (2020, invited).
  11. Time-series learning of latent-space dynamics for reduced-order model closure. Physica D 405 (2020).
  12. Quantifying uncertainties on fission fragment mass yields with mixture density networks. Journal of Physics G 47 (2020).
  13. Compressed convolutional LSTM: an efficient deep learning framework to model high-fidelity 3D turbulence. arXiv (2019).
  14. A deep learning based approach to reduced order modeling for turbulent flow control using LSTM neural networks. arXiv (2018).
  15. Analysis of airfoil stall control using dynamic mode decomposition. Journal of Aircraft 54 (2017).
  16. Model reduction and analysis of deep dynamic stall on a plunging airfoil. Computers & Fluids 129 (2016).

Talks

I speak about trustworthy scientific AI, physics-informed machine learning, sparse sensing, and the practical challenges of putting modern hazard science into emergency operations. For seminar, conference, classroom, or practitioner-focused invitations, please get in touch by email.

Contact

I’m a public-facing point of contact for anyone interested in scientific AI, disaster resilience, or collaboration: researchers, emergency managers, students, and builders alike.