396 citations · 673 across the 27 of their papers we have counts for
27 papers
TWIG: A Time-Causal Wavelet Operator for Autoregressive Forecasting on Irregular Graphs
Subashree Venkatasubramanian, David A. Barajas-Solano, Chuyang Liu +2
We introduce TWIG (Time-Causal Wavelet Operator for Irregular Graphs), a graph-native neural operator for autoregressive surrogate modeling on static irregular graphs. TWIG transfo…
Uncertainty-aware Multi-fidelity Closure via Conditional Normalizing Flows
Jice Zeng, Shady E. Ahmed, David Barajas-Solano +1
Reduced-order models (ROMs) provide efficient surrogates for complex multiscale systems, but their predictive accuracy is often compromised by truncation errors and the inadequate…
Statistical Learning Analysis of Physics-Informed Neural Networks
David A. Barajas-Solano
We study the training and performance of physics-informed learning for initial and boundary value problems (IBVP) with physics-informed neural networks (PINNs) from a statistical l…
Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning
Jice Zeng, David Barajas-Solano, Hui Chen
The performance of machine learning surrogates is critically dependent on data quality and quantity. This presents a major challenge, as high-fidelity (HF) data is often scarce and…
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…
Variational Encoder-Decoders for Learning Latent Representations of Physical Systems
Subashree Venkatasubramanian, David A. Barajas-Solano
We present a deep-learning Variational Encoder-Decoder (VED) framework for learning data-driven low-dimensional representations of the relationship between high-dimensional paramet…
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…
Online Real-time Learning of Dynamical Systems from Noisy Streaming Data: A Koopman Operator Approach
S. Sinha, Sai P. Nandanoori, David Barajas-Solano
Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological n…
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…
Sparsifying Priors for Bayesian Uncertainty Quantification in Model Discovery
Seth M. Hirsh, David A. Barajas-Solano, J. Nathan Kutz
We propose a probabilistic model discovery method for identifying ordinary differential equations (ODEs) governing the dynamics of observed multivariate data. Our method is based o…
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…
A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure
Jacob Roth, David A. Barajas-Solano, Panos Stinis +2
In this work, cascading transmission line failures are studied through a dynamical model of the power system operating under fixed conditions. The power grid is modeled as a stocha…
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…
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Liu Yang, Sean Treichler, Thorsten Kurth +8
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling…
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
Tong Ma, Renke Huang, David Barajas-Solano +2
We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for…
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models
Ramakrishna Tipireddy, David A Barajas-Solano, Alexandre M. Tartakovsky
We use a conditional Karhunen-Loève (KL) model to quantify and reduce uncertainty in a stochastic partial differential equation (SPDE) problem with partially-known space-dependent…
Approximate Bayesian Model Inversion for PDEs with Heterogeneous and State-Dependent Coefficients
David A. Barajas-Solano, Alexandre M. Tartakovsky
We present two approximate Bayesian inference methods for parameter estimation in partial differential equation (PDE) models with space-dependent and state-dependent parameters. We…
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence
Xiu Yang, David Barajas-Solano, Guzel Tartakovsky +1
In this work, we propose a new Gaussian process regression (GPR)-based multifidelity method: physics-informed CoKriging (CoPhIK). In CoKriging-based multifidelity methods, the quan…
Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks
Alexandre M. Tartakovsky, Carlos Ortiz Marrero, Paris Perdikaris +2
We present a physics informed deep neural network (DNN) method for estimating parameters and unknown physics (constitutive relationships) in partial differential equation (PDE) mod…