H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology H-index 36 Citations 12,706 219 World Ranking 3349 National Ranking 48

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary areas of study are Reinforcement learning, Artificial intelligence, Electric power system, Control theory and Mathematical optimization. His work deals with themes such as Batch processing, Optimal control, Electricity market, Benchmark and Best response, which intersect with Reinforcement learning. His Artificial intelligence research incorporates themes from Cognitive radio, Machine learning, Dynamic programming and Industrial engineering.

His Electric power system study combines topics from a wide range of disciplines, such as Control engineering, Control and Stability. His work carried out in the field of Mathematical optimization brings together such families of science as Convergence, Set, Markov decision process, Decision theory and Discretization. His Ensemble learning study combines topics in areas such as Decision tree, Bias–variance tradeoff, Kernel method, Regression analysis and Cut-point.

His most cited work include:

  • Extremely randomized trees (2672 citations)
  • Tree-Based Batch Mode Reinforcement Learning (719 citations)
  • Reinforcement Learning and Dynamic Programming Using Function Approximators (658 citations)

What are the main themes of his work throughout his whole career to date?

Damien Ernst mostly deals with Reinforcement learning, Mathematical optimization, Electric power system, Artificial intelligence and Control theory. His studies in Reinforcement learning integrate themes in fields like Batch processing, Optimal control, Set, State space and Function. His Mathematical optimization research integrates issues from Tree, Markov decision process, AC power and Benchmark.

His study on Electric power system also encompasses disciplines like

  • Control engineering which is related to area like Emergency control,
  • Stability that connect with fields like Transient. His Artificial intelligence study frequently intersects with other fields, such as Machine learning. Damien Ernst combines topics linked to Power with his work on Control theory.

He most often published in these fields:

  • Reinforcement learning (27.32%)
  • Mathematical optimization (23.94%)
  • Electric power system (23.38%)

What were the highlights of his more recent work (between 2018-2021)?

  • Electricity (10.14%)
  • Reinforcement learning (27.32%)
  • Renewable energy (8.73%)

In recent papers he was focusing on the following fields of study:

Electricity, Reinforcement learning, Renewable energy, Environmental economics and Mathematical optimization are his primary areas of study. His Electricity research is multidisciplinary, incorporating perspectives in Electricity generation, Electric power system and Operations research. His Reinforcement learning research is under the purview of Artificial intelligence.

His study ties his expertise on Machine learning together with the subject of Artificial intelligence. The various areas that Damien Ernst examines in his Renewable energy study include Distribution networks, Wind power, Production and Katabatic wind. His study in Mathematical optimization is interdisciplinary in nature, drawing from both Variable and Renewable power generation.

Between 2018 and 2021, his most popular works were:

  • The impact of different COVID-19 containment measures on electricity consumption in Europe (35 citations)
  • Introducing neuromodulation in deep neural networks to learn adaptive behaviours. (8 citations)
  • The role of power-to-gas and carbon capture technologies in cross-sector decarbonisation strategies (7 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary scientific interests are in Electricity, Reinforcement learning, Environmental economics, Artificial intelligence and Electricity generation. His Electricity research incorporates themes from Distributed computing, Electric power system, Outbreak, Electric power distribution and Operations research. His Electric power system study combines topics from a wide range of disciplines, such as Closure, Reliability engineering, Electricity market and Power station.

His Reinforcement learning research is included under the broader classification of Machine learning. His Environmental economics study incorporates themes from Emerging technologies, Global grid, Global wind patterns and Renewable resource, Renewable energy. Damien Ernst has included themes like Algorithmic trading and Trading strategy in his Artificial intelligence study.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Extremely randomized trees

Pierre Geurts;Damien Ernst;Louis Wehenkel.
Machine Learning (2006)

3957 Citations

Tree-Based Batch Mode Reinforcement Learning

Damien Ernst;Pierre Geurts;Louis Wehenkel.
Journal of Machine Learning Research (2005)

1035 Citations

Reinforcement Learning and Dynamic Programming Using Function Approximators

Lucian Busoniu;Robert Babuska;Bart De Schutter;Damien Ernst.
(2010)

991 Citations

Transient Stability of Power Systems: A Unified Approach to Assessment and Control

Mania Pavella;Damien Ernst;Daniel Ruiz-Vega.
(2000)

582 Citations

Transient Stability of Power Systems

Mania Pavella;Damien Ernst;Daniel Ruiz-Vega.
(2000)

426 Citations

Active Management of Low-Voltage Networks for Mitigating Overvoltages Due to Photovoltaic Units

Frederic Olivier;Petros Aristidou;Damien Ernst;Thierry Van Cutsem.
IEEE Transactions on Smart Grid (2016)

223 Citations

Power systems stability control: reinforcement learning framework

D. Ernst;M. Glavic;L. Wehenkel.
IEEE Transactions on Power Systems (2004)

200 Citations

Interior-point based algorithms for the solution of optimal power flow problems

Florin Capitanescu;Mevludin Glavic;Damien Ernst;Louis Wehenkel.
Electric Power Systems Research (2007)

195 Citations

Contingency Filtering Techniques for Preventive Security-Constrained Optimal Power Flow

F. Capitanescu;M. Glavic;D. Ernst;L. Wehenkel.
IEEE Transactions on Power Systems (2007)

189 Citations

Reinforcement Learning Versus Model Predictive Control: A Comparison on a Power System Problem

D. Ernst;M. Glavic;F. Capitanescu;L. Wehenkel.
systems man and cybernetics (2009)

181 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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