Search Results for author: Spencer H. Bryngelson

Found 8 papers, 7 papers with code

Neural networks can be FLOP-efficient integrators of 1D oscillatory integrands

1 code implementation9 Apr 2024 Anshuman Sinha, Spencer H. Bryngelson

We train a feed-forward neural network to compute integrals of highly oscillatory 1D functions.

RoseNNa: A performant, portable library for neural network inference with application to computational fluid dynamics

1 code implementation30 Jul 2023 Ajay Bati, Spencer H. Bryngelson

RoseNNa is a non-invasive, lightweight (1000 lines), and performant tool for neural network inference, with focus on the smaller networks used to augment PDE solvers, like those of CFD, which are typically written in C/C++ or Fortran.

Competitive Physics Informed Networks

1 code implementation23 Apr 2022 Qi Zeng, Yash Kothari, Spencer H. Bryngelson, Florian Schäfer

Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE residual as the loss function.

QBMMlib: A library of quadrature-based moment methods

1 code implementation12 Aug 2020 Spencer H. Bryngelson, Tim Colonius, Rodney O. Fox

QBMMlib is an open source Mathematica package of quadrature-based moment methods and their algorithms.

Computational Physics Fluid Dynamics

Simulation of humpback whale bubble-net feeding models

1 code implementation25 Sep 2019 Spencer H. Bryngelson, Tim Colonius

The acoustic wave behaviors in the spiral interior vary qualitatively with the vocalization frequency and net void fraction.

Fluid Dynamics

MFC: An open-source high-order multi-component, multi-phase, and multi-scale compressible flow solver

1 code implementation24 Jul 2019 Spencer H. Bryngelson, Kevin Schmidmayer, Vedran Coralic, Jomela C. Meng, Kazuki Maeda, Tim Colonius

MFC is an open-source tool for solving multi-component, multi-phase, and bubbly compressible flows.

Computational Physics Fluid Dynamics 76T10, 76T30, 76N15

An assessment of multicomponent flow models and interface capturing schemes for spherical bubble dynamics

1 code implementation19 Mar 2019 Kevin Schmidmayer, Spencer H. Bryngelson, Tim Colonius

We demonstrate the inadequacy of the traditional 5-equation model of Allaire et al. [1] for spherical bubble collapse problems and explain the corresponding advantages of the augmented model of Kapila et al. [2] for representing this phenomenon.

Fluid Dynamics Computational Physics

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