Graph neural networks
Model-based deep learning
Numerics-informed neural networks
Reduced order models
Parareal time integrators based AI
Inverse problems
Celaya, Kirk, Fuentes, Riviere. ''Solutions to elliptic and parabolic problems via finite difference based unsupervised small linear convolutional neural networs'', Computers and Mathematics with Applications, 174, 31-42, 2024, doi
Cangelosi, Heinkenschloss. "Sensitivity of ODE solutions with respect to component functions in the dynamics", arXiv:2411.09655, 2024.
Cox, Segarra, Elvira. "Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks", arXiv:2411.15638, 2024.
Li, Verma, Efimov, Kumar, Segarra. "GLANCE: graph-based learnable digital twin for communication networks", arXiv:2408.09040, 2024.
Celaya, Wang, Fuentes, Riviere. "Learning discontinuous Galerkin solutions to elliptic problems via small linear convolutional neural networks", arXiv:2502.08783, 2025.
Ding, Ren, Zhang. "Coupling deep learning with full waveform inversion", submitted, 2025.
Celaya, Fuentes, Riviere. "An adaptive collocation point strategy for physics informed neural networks via the QR discrete empirical interpolation method", arXiv:2501.07700, 2025.