5 papers
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…