17 citations · 18 across the 8 of their papers we have counts for
5 papers · 1 filter
Model synthesis and identifiability analysis of stiff chemical reaction systems with inVAErt networks
Sreejata Dey, Guoxiang Grayson Tong, Jonathan F. MacArt +1
We consider the problem of learning data-driven replicas for stiff systems of ordinary differential equations arising in chemical kinetics that can be evaluated with high computati…
FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions
Chloe H. Choi, Alison L. Marsden, Daniele E. Schiavazzi
Boundary condition tuning is a fundamental step in patient-specific cardiovascular modeling. Despite an increase in offline training cost, recent methods in data-driven variational…
On the accuracy of implicit neural representations for cardiovascular anatomies and hemodynamic fields
Jubilee Lee, Daniele E. Schiavazzi
Implicit neural representations (INRs, also known as neural fields) have recently emerged as a powerful framework for knowledge representation, synthesis, and compression. By encod…
LINFA: a Python library for variational inference with normalizing flow and annealing
Yu Wang, Emma R. Cobian, Jubilee Lee +3
Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions. We developed LINFA (Library for Inference wi…
Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models
Yu Wang, Fang Liu, Daniele E. Schiavazzi
Fast inference of numerical model parameters from data is an important prerequisite to generate predictive models for a wide range of applications. Use of sampling-based approaches…