National Renewable Energy Laboratory

∙ Golden , United States ∙ 0 followers

155855 The National Renewable Energy Laboratory, located in Golden, Colorado, specializes in renewable energy and energy efficiency research and development.

research

∙ 11/08/2021

Adversarial sampling of unknown and high-dimensional conditional distributions

Many engineering problems require the prediction of realization-to-reali...

Malik Hassanaly, et al. ∙

research

∙ 05/10/2018

An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending

Solar forecasting accuracy is affected by weather conditions, and weathe...

Cong Feng, et al. ∙

research

∙ 07/26/2018

Message-passing neural networks for high-throughput polymer screening

Machine learning methods have shown promise in predicting molecular prop...

Peter C. St. John, et al. ∙

research

∙ 09/16/2018

Low synchronization GMRES algorithms

Communication-avoiding and pipelined variants of Krylov solvers are crit...

Kasia Świrydowicz, et al. ∙

research

∙ 10/16/2018

From Deep to Physics-Informed Learning of Turbulence: Diagnostics

We describe physical tests validating progress made toward acceleration ...

Ryan King, et al. ∙

research

∙ 10/29/2018

Reduced models of point vortex systems

Nonequilibrium statistical models of point vortex systems are constructe...

Jonathan Maack, et al. ∙

research

∙ 02/28/2019

A block-random algorithm for learning on distributed, heterogeneous data

Most deep learning models are based on deep neural networks with multipl...

Prakash Mohan, et al. ∙

research

∙ 09/27/2019

Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow

In this paper, we develop an online method that leverages machine learni...

Ahmed Zamzam, et al. ∙

research

∙ 03/06/2020

Distributed macroscopic traffic simulation with Open Traffic Models

This paper presents OTM-MPI, an extension of the Open Traffic Models pla...

Gabriel Gomes, et al. ∙

research

∙ 04/13/2020

K-spin Hamiltonian for quantum-resolvable Markov decision processes

The Markov decision process is the mathematical formalization underlying...

Eric B. Jones, et al. ∙

research

∙ 04/13/2020

Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition

As electric grids experience high penetration levels of renewable genera...

Ahmed S. Zamzam, et al. ∙

research

∙ 07/26/2020

Convex Decreasing Algorithms: Distributed Synthesis and Finite-time Termination in Higher Dimension

We introduce a general mathematical framework for distributed algorithms...

James Melbourne, et al. ∙

research

∙ 11/01/2021

OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets

Increasing levels of renewable generation motivate a growing interest in...

Trager Joswig-Jones, et al. ∙

research

∙ 11/10/2021

PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems

We present the PowerGridworld software package to provide users with a l...

David Biagioni, et al. ∙

research

∙ 11/18/2021

ILU Smoothers for C-AMG with Scaled Triangular Factors

ILU smoothers can be effective in the algebraic multigrid V-cycle. Howev...

Stephen Thomas, et al. ∙

research

∙ 12/28/2021

GANISP: a GAN-assisted Importance SPlitting Probability Estimator

Designing manufacturing processes with high yield and strong reliability...

Malik Hassanaly, et al. ∙

research

∙ 12/28/2021

Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows

Improvements in computational and experimental capabilities are rapidly ...

Malik Hassanaly, et al. ∙

research

∙ 01/12/2022

Grassmannian Shape Representations for Aerodynamic Applications

Airfoil shape design is a classical problem in engineering and manufactu...

Olga A. Doronina, et al. ∙

National Renewable Energy Laboratory

Featured Researchers

Kasia Świrydowicz

Stephen Thomas

Ahmed S. Zamzam

Andrey Bernstein

Malik Hassanaly

Wesley Jones

Yajing Liu

Andrew Glaws

Ryan King

Govind Saraswat

Ryan N. King

Adversarial sampling of unknown and high-dimensional conditional distributions

An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending

Message-passing neural networks for high-throughput polymer screening

Low synchronization GMRES algorithms

From Deep to Physics-Informed Learning of Turbulence: Diagnostics

Reduced models of point vortex systems

A block-random algorithm for learning on distributed, heterogeneous data

Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow

Distributed macroscopic traffic simulation with Open Traffic Models

K-spin Hamiltonian for quantum-resolvable Markov decision processes

Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition

Convex Decreasing Algorithms: Distributed Synthesis and Finite-time Termination in Higher Dimension

OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets

PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems

ILU Smoothers for C-AMG with Scaled Triangular Factors

GANISP: a GAN-assisted Importance SPlitting Probability Estimator

Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows

Grassmannian Shape Representations for Aerodynamic Applications