30 citations · 31 across the 4 of their papers we have counts for
5 papers
VAE-DNN: Energy-Efficient Trainable-by-Parts Surrogate Model For Parametric Partial Differential Equations
Yifei Zong, Alexandre M. Tartakovsky
We propose a trainable-by-parts surrogate model for solving forward and inverse parameterized nonlinear partial differential equations. Like several other surrogate and operator le…
Mathematics of Digital Twins and Transfer Learning for PDE Models
Yifei Zong, Alexandre Tartakovsky
We define a digital twin (DT) of a physical system governed by partial differential equations (PDEs) as a model for real-time simulations and control of the system behavior under c…
Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation
Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky
We propose a randomized physics-informed neural network (PINN) or rPINN method for uncertainty quantification in inverse partial differential equation (PDE) problems with noisy dat…
Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems
Yifei Zong, David Barajas-Solano, Alexandre M. Tartakovsky
We propose a physics-informed machine learning method for uncertainty quantification in high-dimensional inverse problems. In this method, the states and parameters of partial diff…
Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions
Yifei Zong, QiZhi He, Alexandre M. Tartakovsky
In this paper, we develop a physics-informed neural network (PINN) model for parabolic problems with a sharply perturbed initial condition. As an example of a parabolic problem, we…