Search Results for author:
Found 7 papers, 1 papers with code
Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data
We propose a fast probabilistic framework for identifying differential equations governing the dynamics of observed data.
Physics-informed compressed sensing for PC-MRI: an inverse Navier-Stokes problem
This prior information is updated using the Navier-Stokes problem, an energy-based segmentation functional, and by requiring that the reconstruction is consistent with the $k$-space signals.
Simultaneous boundary shape estimation and velocity field de-noising in Magnetic Resonance Velocimetry using Physics-informed Neural Networks
In this paper, we present a physics-informed neural network that instead uses the noisy MRV data alone to simultaneously infer the most likely boundary shape and de-noised velocity field.
Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning
The 100 MW cryogenic liquid oxygen/hydrogen multi-injector combustor BKD operated by the DLR Institute of Space Propulsion is a research platform that allows the study of thermoacoustic instabilities under realistic conditions, representative of small upper stage rocket engines.
Online parameter inference for the simulation of a Bunsen flame using heteroscedastic Bayesian neural network ensembles
Heteroscedastic Bayesian neural network ensembles are trained on a library of 1. 7 million flame fronts simulated in LSGEN2D, a G-equation solver, to learn the Bayesian posterior distribution of the model parameters given observations.
Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles
The trained neural networks are then used to infer model parameters from real videos of a premixed Bunsen flame captured using a high-speed camera in our lab.
Combined State and Parameter Estimation in Level-Set Methods
Reduced-order models based on level-set methods are widely used tools to qualitatively capture and track the nonlinear dynamics of an interface.
