A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows
We propose a novel modular inference approach combining two different generative models—
generative adversarial networks (GAN) and normalizing flows—to approximate the posterior …
generative adversarial networks (GAN) and normalizing flows—to approximate the posterior …
[HTML][HTML] Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network
Abstract Background and Objectives Techniques for imaging the mechanical properties of
cells are needed to study how cell mechanics influence cell function and disease …
cells are needed to study how cell mechanics influence cell function and disease …
Conditional score-based diffusion models for solving inverse problems in mechanics
We propose a framework to perform Bayesian inference using conditional score-based
diffusion models to solve a class of inverse problems in mechanics involving the inference of …
diffusion models to solve a class of inverse problems in mechanics involving the inference of …
Conditional score-based diffusion models for solving inverse elasticity problems
We propose a framework to perform Bayesian inference using conditional score-based
diffusion models to solve a class of inverse problems in mechanics involving the inference of …
diffusion models to solve a class of inverse problems in mechanics involving the inference of …
Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances
In inverse problems, many conditional generative models approximate the posterior
measure by minimizing a distance between the joint measure and its learned approximation …
measure by minimizing a distance between the joint measure and its learned approximation …
Conditional score-based generative models for solving physics-based inverse problems
We propose to sample from high-dimensional posterior distributions arising in physics-
based inverse problems using conditional score-based generative models. The proposed …
based inverse problems using conditional score-based generative models. The proposed …
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces
Solving inverse problems in scientific and engineering fields has long been intriguing and
holds great potential for many applications, yet most techniques still struggle to address …
holds great potential for many applications, yet most techniques still struggle to address …
Learning WENO for entropy stable schemes to solve conservation laws
P Charles, D Ray - arXiv preprint arXiv:2403.14848, 2024 - arxiv.org
Entropy conditions play a crucial role in the extraction of a physically relevant solution for a
system of conservation laws, thus motivating the construction of entropy stable schemes that …
system of conservation laws, thus motivating the construction of entropy stable schemes that …
Probabilistic brain extraction in MR images via conditional generative adversarial networks
Brain extraction, or the task of segmenting the brain in MR images, forms an essential step
for many neuroimaging applications. These include quantifying brain tissue volumes …
for many neuroimaging applications. These include quantifying brain tissue volumes …
[HTML][HTML] U-Net for temperature estimation from simulated infrared images in tokamaks
Surface temperature measurement is critical for the safety and proper operation of nuclear
fusion reactors such as tokamaks, which operate at high temperature [100–3600° C] in the …
fusion reactors such as tokamaks, which operate at high temperature [100–3600° C] in the …