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.
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
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.
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.
Extremely randomized trees
Pierre Geurts;Damien Ernst;Louis Wehenkel.
Machine Learning (2006)
Tree-Based Batch Mode Reinforcement Learning
Damien Ernst;Pierre Geurts;Louis Wehenkel.
Journal of Machine Learning Research (2005)
Reinforcement Learning and Dynamic Programming Using Function Approximators
Lucian Busoniu;Robert Babuska;Bart De Schutter;Damien Ernst.
Transient Stability of Power Systems: A Unified Approach to Assessment and Control
Mania Pavella;Damien Ernst;Daniel Ruiz-Vega.
Transient Stability of Power Systems
Mania Pavella;Damien Ernst;Daniel Ruiz-Vega.
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)
Power systems stability control: reinforcement learning framework
D. Ernst;M. Glavic;L. Wehenkel.
IEEE Transactions on Power Systems (2004)
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)
Contingency Filtering Techniques for Preventive Security-Constrained Optimal Power Flow
F. Capitanescu;M. Glavic;D. Ernst;L. Wehenkel.
IEEE Transactions on Power Systems (2007)
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)
Profile was last updated on December 6th, 2021.
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