Georgios Kissas

According to our database, Georgios Kissas authored at least 11 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Neuro-Symbolic ODE Discovery with Latent Grammar Flow.

Karin L. Yu, Eleni N. Chatzi, Georgios Kissas

CoRR, April, 2026

Phaedra: Learning High-Fidelity Discrete Tokenization for the Physical Science.

Levi E. Lingsch, Georgios Kissas, Johannes Jakubik, Siddhartha Mishra

CoRR, February, 2026

2025

Grammar-based Ordinary Differential Equation Discovery.

Karin L. Yu, Eleni N. Chatzi, Georgios Kissas

CoRR, April, 2025

Neuro-Symbolic AI for Analytical Solutions of Differential Equations.

Orestis Oikonomou, Levi E. Lingsch, Dana Grund, Siddhartha Mishra, Georgios Kissas

CoRR, February, 2025

2024

Accelerated Patient-Specific Calibration via Differentiable Hemodynamics Simulations.

Diego Renner, Georgios Kissas

CoRR, 2024

FUSE: Fast Unified Simulation and Estimation for PDEs.

Levi E. Lingsch, Dana Grund, Siddhartha Mishra, Georgios Kissas

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Variational Autoencoding Neural Operators.

Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris

Proceedings of the International Conference on Machine Learning, 2023

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

Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors.

Yibo Yang, Georgios Kissas, Paris Perdikaris

CoRR, 2022

NOMAD: Nonlinear Manifold Decoders for Operator Learning.

Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2019

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