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Found 10 papers, 8 papers with code
A Probabilistic Neural Twin for Treatment Planning in Peripheral Pulmonary Artery Stenosis
The substantial computational cost of high-fidelity models in numerical hemodynamics has, so far, relegated their use mainly to offline treatment planning.
InVAErt networks: a data-driven framework for model synthesis and identifiability analysis
Use of generative models and deep learning for physics-based systems is currently dominated by the task of emulation.
LINFA: a Python library for variational inference with normalizing flow and annealing
Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions.
Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records
We use normalizing flows (NF), a family of deep generative models, to estimate the probability density of a dataset with differential privacy (DP) guarantees, from which privacy-preserving synthetic data are generated.
Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue
We propose a data-driven framework to increase the computational efficiency of the explicit finite element method in the structural analysis of soft tissue.
Multifidelity data fusion in convolutional encoder/decoder networks
We analyze the regression accuracy of convolutional neural networks assembled from encoders, decoders and skip connections and trained with multifidelity data.
AdaAnn: Adaptive Annealing Scheduler for Probability Density Approximation
We demonstrate the computational efficiency of the AdaAnn scheduler for variational inference with normalizing flows on a number of examples, including density approximation and parameter estimation for dynamical systems.
An analysis of reconstruction noise from undersampled 4D flow MRI
Novel Magnetic Resonance (MR) imaging modalities can quantify hemodynamics but require long acquisition times, precluding its widespread use for early diagnosis of cardiovascular disease.
Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models
To reduce the computational cost without sacrificing inferential accuracy, we propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and surrogate model parameters.
An ensemble solver for segregated cardiovascular FSI
Computational models are increasingly used for diagnosis and treatment of cardiovascular disease.
Computational Engineering, Finance, and Science Numerical Analysis Numerical Analysis
