466 citations · 1.7k across the 54 of their papers we have counts for
55 papers
Parameter Estimation in River Transport Models With Immobile Phase Exchange Using Dimensional Analysis and Reduced-Order Models
Manuel M. Reyna, Alexandre M. Tartakovsky
We propose a framework for parameter estimation in river transport models using breakthrough curve data, which we refer to as Dimensionless Synthetic Transport Estimation (DSTE). W…
Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data
Jice Zeng, Yuanzhe Wang, Alexandre M. Tartakovsky +1
We present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary n…
Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models
Yuanzhe Wang, Alexandre M. Tartakovsky
We propose an approximate Bayesian method for quantifying the total uncertainty in inverse PDE solutions obtained with machine learning surrogate models, including operator learnin…
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…
Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling
Yu-Hong Yeung, Ramakrishna Tipireddy, David A. Barajas-Solano +1
We propose a methodology for improving the accuracy of surrogate models of the observable response of physical systems as a function of the systems' spatially heterogeneous paramet…
Gaussian process regression and conditional Karhunen-Loéve models for data assimilation in inverse problems
Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
We present a model inversion algorithm, CKLEMAP, for data assimilation and parameter estimation in partial differential equation models of physical systems with spatially heterogen…
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine +27
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powe…
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…
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery
QiZhi He, Yucheng Fu, Panos Stinis +1
Numerical modeling and simulation have become indispensable tools for advancing a comprehensive understanding of the underlying mechanisms and cost-effective process optimization a…
Machine Learning in Heterogeneous Porous Materials
Marta D'Elia, Hang Deng, Cedric Fraces +21
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material scienc…
Autonomous Inversion of In Situ Deformation Measurement Data for CO2 Storage Decision Support
Jeff Burghardt, Ting Bao, Kailai Xu +2
Current methods of estimating the change in stress caused by injecting fluid into subsurface formations require choosing the type of constitutive model and the model parameters bas…
Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems
Yu-Hong Yeung, David A. Barajas-Solano, Alexandre M. Tartakovsky
We develop a physics-informed machine learning approach for large-scale data assimilation and parameter estimation and apply it for estimating transmissivity and hydraulic head in…
Physics-informed CoKriging model of a redox flow battery
Amanda A. Howard, Alexandre M. Tartakovsky
Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently, however, there is a need for fast and accurate models of the charge-disch…
Physics-constrained deep neural network method for estimating parameters in a redox flow battery
QiZhi He, Panos Stinis, Alexandre Tartakovsky
In this paper, we present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (V…
Time-dependent stochastic basis adaptation for uncertainty quantification
Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
We extend stochastic basis adaptation and spatial domain decomposition methods to solve time varying stochastic partial differential equations (SPDEs) with a large number of input…
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…
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
QiZhi He, David Brajas-Solano, Guzel Tartakovsky +1
Data assimilation for parameter and state estimation in subsurface transport problems remains a significant challenge due to the sparsity of measurements, the heterogeneity of poro…
Physics-Informed Machine Learning with Conditional Karhunen-Loève Expansions
Alexandre M. Tartakovsky, David A. Barajas-Solano, Qizhi He
We present a new physics-informed machine learning approach for the inversion of PDE models with heterogeneous parameters. In our approach, the space-dependent partially-observed p…