no code implementations • 23 Feb 2024 • Lloyd Fung, Urban Fasel, Matthew P. Juniper
We propose a fast probabilistic framework for identifying differential equations governing the dynamics of observed data.
no code implementations • 4 Jul 2022 • Alexandros Kontogiannis, Matthew P. Juniper
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.
no code implementations • 16 Jul 2021 • Ushnish Sengupta, Alexandros Kontogiannis, Matthew P. Juniper
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.
no code implementations • 1 Jul 2021 • Ushnish Sengupta, Günther Waxenegger-Wilfing, Jan Martin, Justin Hardi, Matthew P. Juniper
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.
1 code implementation • 26 Apr 2021 • Maximilian L. Croci, Ushnish Sengupta, Matthew P. Juniper
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.
no code implementations • 11 Oct 2020 • Ushnish Sengupta, Maximilian L. Croci, Matthew P. Juniper
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.
no code implementations • 1 Mar 2019 • Hans Yu, Matthew P. Juniper, Luca Magri
Reduced-order models based on level-set methods are widely used tools to qualitatively capture and track the nonlinear dynamics of an interface.