Search Results for author: Nicholas Lubbers

Found 11 papers, 4 papers with code

Multi-Scale Neural Networks for to Fluid Flow in 3D Porous Media

no code implementations10 Feb 2021 Javier Santos, Ying Yin, Honggeun Jo, Wen Pan, Qinjun Kang, Hari Viswanathan, Masa Prodanovic, Michael Pyrcz, Nicholas Lubbers

The permeability of complex porous materials can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive.

Modeling nanoconfinement effects using active learning

no code implementations6 May 2020 Javier E. Santos, Mohammed Mehana, Hao Wu, Masa Prodanovic, Michael J. Pyrcz, Qinjun Kang, Nicholas Lubbers, Hari Viswanathan

At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions.

Active Learning

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

Less is more: sampling chemical space with active learning

3 code implementations28 Jan 2018 Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg

In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials.

Active Learning

Hierarchical modeling of molecular energies using a deep neural network

no code implementations29 Sep 2017 Nicholas Lubbers, Justin S. Smith, Kipton Barros

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations.

Drug Discovery Formation Energy

Machine Learning Predicts Laboratory Earthquakes

1 code implementation19 Feb 2017 Bertrand Rouet-Leduc, Claudia Hulbert, Nicholas Lubbers, Kipton Barros, Colin Humphreys, Paul A. Johnson

Forecasting fault failure is a fundamental but elusive goal in earthquake science.


Inferring low-dimensional microstructure representations using convolutional neural networks

no code implementations8 Nov 2016 Nicholas Lubbers, Turab Lookman, Kipton Barros

We use activations in a pre-trained convolutional neural network to provide a high-dimensional characterization of a set of synthetic microstructural images.

BIG-bench Machine Learning

The Effective Field Theory of Dark Matter Direct Detection

3 code implementations15 Mar 2012 A. Liam Fitzpatrick, Wick Haxton, Emanuel Katz, Nicholas Lubbers, Yiming Xu

We extend and explore the general non-relativistic effective theory of dark matter (DM) direct detection.

High Energy Physics - Phenomenology Cosmology and Nongalactic Astrophysics

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