2.5k citations · 6.9k across the 23 of their papers we have counts for
4 papers · 2 filters
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…
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…