Online appendix containing supplementary data and code to reproduce the simulation results in A. Venzke, G. Qu, S. Low and S. Chatzivasileiadis, "Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks". See readme.txt for a detailed description. When publishing results based on this data/code, please cite: A. Venzke, G. Qu, S. Low and S. Chatzivasileiadis, "Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks", 2020. Available online: https://arxiv.org/abs/2006.1102
The simulation of biological neural networks (BNN) is essential to neuroscience. The complexity of t...
The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electri...
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International audienceRecent trends in power systems and those envisioned for the next few decades p...
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This is a technical appendix designed to support "Learning to Play 3 3 Games: Neural Networks ...
Information measures are often used to assess the efficacy of neural networks, and learning rules ca...
This archive contains the source code to reproduce most figures from the book "Statistical field the...
Deep learning approaches for the Alternating Current-Optimal Power Flow (AC-OPF) problem are under a...
The simulation of biological neural networks (BNN) is essential to neuroscience. The complexity of t...
The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electri...
Various researchers have used one hidden layer neural networks (weighted sums of sigmoids) to find t...
This paper introduces for the first time a framework to obtain provable worst-case guarantees for ne...
Optimal power flow (OPF) is at the heart of many power system operation tools and market clearing pr...
The repo contains data for the paper @article{de2022cost, title={The Cost-Accuracy Trade-Off In O...
Normalizing flows are a popular approach for constructing probabilistic and generative models. Howev...
Power flow analysis is an important tool in power engineering for planning and operating power syste...
. We present a method for determining the globally optimal on-line learning rule for a soft committe...
International audienceRecent trends in power systems and those envisioned for the next few decades p...
We derive global H 1 optimal training algorithms for neural networks. These algorithms guarantee t...
This is a technical appendix designed to support "Learning to Play 3 3 Games: Neural Networks ...
Information measures are often used to assess the efficacy of neural networks, and learning rules ca...
This archive contains the source code to reproduce most figures from the book "Statistical field the...
Deep learning approaches for the Alternating Current-Optimal Power Flow (AC-OPF) problem are under a...
The simulation of biological neural networks (BNN) is essential to neuroscience. The complexity of t...
The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electri...
Various researchers have used one hidden layer neural networks (weighted sums of sigmoids) to find t...