Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning. - Georgios Kissas; Yibo Yang; Eileen Hwuang; Walter R. Witschey; John A. Detre; Paris Perdikaris (CoRR, 2019)

NOMAD: Nonlinear Manifold Decoders for Operator Learning. - Jacob H. Seidman; Georgios Kissas; Paris Perdikaris; George J. Pappas (NeurIPS, 2022)

Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors. - Yibo Yang; Georgios Kissas; Paris Perdikaris (CoRR, 2022)

Learning Operators with Coupled Attention. - Georgios Kissas; Jacob H. Seidman; Leonardo Ferreira Guilhoto; Victor M. Preciado; George J. Pappas; Paris Perdikaris (J. Mach. Learn. Res., 2022)

Variational Autoencoding Neural Operators. - Jacob H. Seidman; Georgios Kissas; George J. Pappas; Paris Perdikaris (ICML, 2023)

FUSE: Fast Unified Simulation and Estimation for PDEs. - Levi E. Lingsch; Dana Grund; Siddhartha Mishra; Georgios Kissas (NeurIPS, 2024)

Accelerated Patient-Specific Calibration via Differentiable Hemodynamics Simulations. - Diego Renner; Georgios Kissas (CoRR, 2024)

Neuro-Symbolic AI for Analytical Solutions of Differential Equations. - Orestis Oikonomou; Levi E. Lingsch; Dana Grund; Siddhartha Mishra; Georgios Kissas (CoRR, 2025)

Grammar-based Ordinary Differential Equation Discovery. - Karin L. Yu; Eleni N. Chatzi; Georgios Kissas (CoRR, 2025)