2.5k citations · 6.9k across the 23 of their papers we have counts for
7 papers · 1 filter
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…
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…
Solution multiplicity and effects of data and eddy viscosity on Navier-Stokes solutions inferred by physics-informed neural networks
Zhicheng Wang, Xuhui Meng, Xiaomo Jiang +2
Physics-informed neural networks (PINNs) have emerged as a new simulation paradigm for fluid flows and are especially effective for inverse and hybrid problems. However, vanilla PI…
Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines
Kamaljyoti Nath, Xuhui Meng, Daniel J Smith +1
This paper presents a physics-informed neural network (PINN) approach for monitoring the health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown para…
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…
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…