Zongren Zou

According to our database, Zongren Zou authored at least 18 papers between 2022 and 2024.

Collaborative distances:

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

2024

NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators.

Zongren Zou, Xuhui Meng, Apostolos F. Psaros, George E. Karniadakis

SIAM Rev., February, 2024

Leveraging Multitime Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems.

Paula X. Chen, Tingwei Meng, Zongren Zou, Jérôme Darbon, George Em Karniadakis

SIAM J. Sci. Comput., 2024

Leveraging Viscous Hamilton-Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning.

Zongren Zou, Tingwei Meng, Paula X. Chen, Jérôme Darbon, George Em Karniadakis

SIAM/ASA J. Uncertain. Quantification, 2024

Correcting model misspecification in physics-informed neural networks (PINNs).

Zongren Zou, Xuhui Meng, George Em Karniadakis

J. Comput. Phys., 2024

From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning.

Juan Diego Toscano, Vivek Oommen, Alan John Varghese, Zongren Zou, Nazanin Ahmadi Daryakenari, Chenxi Wu, George Em Karniadakis

CoRR, 2024

HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models.

Tingwei Meng, Zongren Zou, Jérôme Darbon, George Em Karniadakis

CoRR, 2024

Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology.

Mario De Florio, Zongren Zou, Daniele E. Schiavazzi, George Em Karniadakis

CoRR, 2024

NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements.

Khemraj Shukla, Zongren Zou, Chi Hin Chan, Additi Pandey, Zhicheng Wang, George Em Karniadakis

CoRR, 2024

A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks.

Khemraj Shukla, Juan Diego Toscano, Zhicheng Wang, Zongren Zou, George Em Karniadakis

CoRR, 2024

Large scale scattering using fast solvers based on neural operators.

Zongren Zou, Adar Kahana, Enrui Zhang, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis

CoRR, 2024

Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning.

Paula X. Chen, Tingwei Meng, Zongren Zou, Jérôme Darbon, George Em Karniadakis

Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024

2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons.

Apostolos F. Psaros, Xuhui Meng, Zongren Zou, Ling Guo, George Em Karniadakis

J. Comput. Phys., March, 2023

Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators.

Zongren Zou, Xuhui Meng, George Em Karniadakis

CoRR, 2023

Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression.

Zhen Zhang, Zongren Zou, Ellen Kuhl, George Em Karniadakis

CoRR, 2023

A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes.

Minglang Yin, Zongren Zou, Enrui Zhang, Cristina Cavinato, Jay D. Humphrey, George Em Karniadakis

CoRR, 2023

Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems.

Paula X. Chen, Tingwei Meng, Zongren Zou, Jérôme Darbon, George Em Karniadakis

CoRR, 2023

L-HYDRA: Multi-Head Physics-Informed Neural Networks.

Zongren Zou, George Em Karniadakis

CoRR, 2023

2022

Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems.

Kevin Linka, Amelie Schäfer, Xuhui Meng, Zongren Zou, George Em Karniadakis, Ellen Kuhl

CoRR, 2022