466 citations · 1.7k across the 54 of their papers we have counts for
6 papers · 1 filter
Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations
QiZhi He, Alexandre M. Tartakovsky
We propose a discretization-free approach based on the physics-informed neural network (PINN) method for solving coupled advection-dispersion and Darcy flow equations with space-de…
An efficient epistemic uncertainty quantification algorithm for a class of stochastic models: A post-processing and domain decomposition framework
Mahadevan Ganesh, Stuart C Hawkins, Alexandre Tartakovsky +1
Partial differential equations (PDEs) are fundamental for theoretically describing numerous physical processes that are based on some input fields in spatial configurations. Unders…
Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids
Tong Ma, David Alonso Barajas-Solano, Ramakrishna Tipireddy +1
Real-time state estimation and forecasting is critical for efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is pre…
Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems
Daniel Dylewsky, David Barajas-Solano, Tong Ma +2
Time series forecasting remains a central challenge problem in almost all scientific disciplines. We introduce a novel load forecasting method in which observed dynamics are modele…
Learning Unknown Physics of non-Newtonian Fluids
Brandon Reyes, Amanda A. Howard, Paris Perdikaris +1
We extend the physics-informed neural network (PINN) method to learn viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) using only velocity…
Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning
Kailai Xu, Alexandre M. Tartakovsky, Jeff Burghardt +1
We propose a novel approach to model viscoelasticity materials using neural networks, which capture rate-dependent and nonlinear constitutive relations. However, inputs and outputs…