396 citations · 673 across the 27 of their papers we have counts for
13 papers · 1 filter
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…
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 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…
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…