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