most citedNeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators

13 citations · 32 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG20236 cited

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

Zongren Zou, Xuhui Meng, George Em Karniadakis

Uncertainty quantification (UQ) in scientific machine learning (SciML) becomes increasingly critical as neural networks (NNs) are being widely adopted in addressing complex problem…

cs.LG20234 cited

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

Zongren Zou, Xuhui Meng, George Em Karniadakis

Data-driven discovery of governing equations in computational science has emerged as a new paradigm for obtaining accurate physical models and as a possible alternative to theoreti…

cs.LG20234 cited

Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading

Minglei Lu, Ali Mohammadi, Zhaoxu Meng +3

Additive manufacturing has been recognized as an industrial technological revolution for manufacturing, which allows fabrication of materials with complex three-dimensional (3D) st…

math.NA2023

Variational inference in neural functional prior using normalizing flows: Application to differential equation and operator learning problems

Xuhui Meng

Physics-informed deep learning have recently emerged as an effective tool for leveraging both observational data and available physical laws. Physics-informed neural networks (PINN…

math.NA20235 cited

Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions

Zhiping Mao, Xuhui Meng

We consider solving the forward and inverse PDEs which have sharp solutions using physics-informed neural networks (PINNs) in this work. In particular, to better capture the sharpn…

cs.LG202213 cited

NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators

Zongren Zou, Xuhui Meng, Apostolos F Psaros +1

Uncertainty quantification (UQ) in machine learning is currently drawing increasing research interest, driven by the rapid deployment of deep neural networks across different field…