Results 241 to 250 of about 953,953 (275)

Predicting the stability of ternary intermetallics with density functional theory and machine learning

The Journal of Chemical Physics, 2018
We use a combination of machine learning techniques and high-throughput density-functional theory calculations to explore ternary compounds with the AB2C2 composition. We chose the two most common intermetallic prototypes for this composition, namely, the tI10-CeAl2Ga2 and the tP10-FeMo2B2 structures.
Jonathan Schmidt, Liming Chen, Silvana Botti, Miguel A. L. Marques   +3 more
openaire   +4 more sources

Stability Certificates for Neural Network Learning-based Controllers using Robust Control Theory

2021 American Control Conference (ACC), 2021
Providing stability guarantees for controllers that use neural networks can be challenging. Robust control theoretic tools are used to derive a framework for providing nominal stability guarantees – stability guarantees for a known nominal system – controlled by a learning-based neural network controller.
Hoang Hai Nguyen, Tim Zieger, Sandra C. Wells, Anastasia Nikolakopoulou, Richard D. Braatz, Rolf Findeisen   +5 more
openaire   +3 more sources

Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning

Chemistry of Materials, 2017
We perform a large scale benchmark of machine learning methods for the prediction of the thermodynamic stability of solids. We start by constructing a data set that comprises density functional theory calculations of around 250000 cubic perovskite systems.
Jonathan Schmidt, Jingming Shi, Pedro Borlido, Liming Chen, Silvana Botti, Miguel A. L. Marques   +5 more
openaire   +3 more sources

Iterative learning control design based on feedback linearization and nonlinear repetitive process stability theory

2016 IEEE 55th Conference on Decision and Control (CDC), 2016
Iterative learning control laws can be applied to systems that execute the same finite duration task over and over again. Previous research for linear dynamics has used the stability theory of linear repetitive processes to design control laws that have been experimentally verified.
Pavel V. Pakshin, Julia P. Emelianova, Mikhail A. Emelianov, Krzysztof Galkowski, Eric Rogers   +4 more
openaire   +3 more sources

Further results on dynamic iterative learning control law design using repetitive process stability theory

2017 10th International Workshop on Multidimensional (nD) Systems (nDS), 2017
Iterative learning control can be applied to systems that execute the same finite duration task over and over again. This method control has been applied to many engineering systems, such as gantry robots and electrical motors. This paper gives further results on the design of dynamic iterative learning control laws using the repetitive process setting
Lukasz Hladowski, Krzysztof Galkowski, Eric Rogers   +2 more
openaire   +3 more sources

Improvement of Power Systems Stability Using a New Learning Algorithm Based on Lyapunov Theory for Neural Network

Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2017
In this paper, a new learning algorithm based on Lyapunov stability theory for neural networks is used to improve the power system stability. During the online control process, the identification of system is not necessary, because of learning ability of the proposed controller.
Mehdi Arab Sadegh, Mohsen Farahani
openaire   +3 more sources

Actor-Critic Reinforcement Learning for Control With Stability Guarantee

IEEE Robotics and Automation Letters, 2020
Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigation to end-to-end visual manipulation.
Minghao Han, Lixian Zhang, Wei Pan
exaly   +4 more sources

Predicting the thermodynamic stability of perovskite oxides using machine learning models

Computational Materials Science, 2018
Perovskite materials have become ubiquitous in many technologically relevant applications, ranging from catalysts in solid oxide fuel cells to light absorbing layers in solar photovoltaics.
Ryan Jacobs, Dane Morgan
exaly   +3 more sources

Deep Lyapunov Learning: Embedding the Lyapunov Stability Theory in Interpretable Neural Networks for Transient Stability Assessment

IEEE Transactions on Power Systems
Jiacheng Liu, Jun Liu, Rudai Yan, Tao Ding   +3 more
openaire   +3 more sources

STABILITY RESULTS IN LEARNING THEORY

Analysis and Applications, 2005
The problem of proving generalization bounds for the performance of learning algorithms can be formulated as a problem of bounding the bias and variance of estimators of the expected error. We show how various stability assumptions can be employed for this purpose.
Rakhlin, Alexander, Mukherjee, Sayan, Poggio, Tomaso   +2 more
openaire   +2 more sources