Search Results for author: Daniel Livescu

Found 8 papers, 2 papers with code

Regression-based projection for learning Mori-Zwanzig operators

no code implementations10 May 2022 Yen Ting Lin, Yifeng Tian, Danny Perez, Daniel Livescu

We propose to adopt statistical regression as the projection operator to enable data-driven learning of the operators in the Mori--Zwanzig formalism.

Physics Informed Machine Learning of SPH: Machine Learning Lagrangian Turbulence

1 code implementation25 Oct 2021 Michael Woodward, Yifeng Tian, Criston Hyett, Chris Fryer, Daniel Livescu, Mikhail Stepanov, Michael Chertkov

Smoothed particle hydrodynamics (SPH) is a mesh-free Lagrangian method for obtaining approximate numerical solutions of the equations of fluid dynamics; which has been widely applied to weakly- and strongly compressible turbulence in astrophysics and engineering applications.

Physics-informed machine learning

Objective discovery of dominant dynamical processes with intelligible machine learning

1 code implementation21 Jun 2021 Bryan E. Kaiser, Juan A. Saenz, Maike Sonnewald, Daniel Livescu

The advent of big data has vast potential for discovery in natural phenomena ranging from climate science to medicine, but overwhelming complexity stymies insight.

Dimensionality Reduction

Wavelet-Powered Neural Networks for Turbulence

no code implementations ICLR Workshop DeepDiffEq 2019 Arvind T. Mohan, Daniel Livescu, Michael Chertkov

One of the fundamental driving phenomena for applications in engineering, earth sciences and climate is fluid turbulence.

Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence

no code implementations31 Jan 2020 Arvind T. Mohan, Nicholas Lubbers, Daniel Livescu, Michael Chertkov

In the recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences.

Computational Physics

Leveraging Bayesian Analysis To Improve Accuracy of Approximate Models

no code implementations20 May 2019 Balasubramanya T. Nadiga, Chiyu Jiang, Daniel Livescu

We focus on improving the accuracy of an approximate model of a multiscale dynamical system that uses a set of parameter-dependent terms to account for the effects of unresolved or neglected dynamics on resolved scales.

Variational Inference

Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence

no code implementations28 Feb 2019 Arvind Mohan, Don Daniel, Michael Chertkov, Daniel Livescu

High-fidelity modeling of turbulent flows is one of the major challenges in computational physics, with diverse applications in engineering, earth sciences and astrophysics, among many others.

Fluid Dynamics Chaotic Dynamics Computational Physics

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