Scientific Applications of Neural Nets
Scientific Applications of Neural Nets Proceedings of the 194th W.E. Heraeus Seminar Held at Bad Honnef, Germany, 11–13 May 1998 / [electronic resource] :
edited by John W. Clark, Thomas Lindenau, Manfred L. Ristig.
- XIII, 290 p. 78 illus., 6 illus. in color. online resource.
- Lecture Notes in Physics, 522 0075-8450 ; .
- Lecture Notes in Physics, 522 .
Neural networks: New tools for modelling and data analysis in science -- Adaptive optics: Neural network wavefront sensing, reconstruction, and prediction -- Nuclear physics with neural networks -- Using neural networks to learn energy corrections in hadronic calorimeters -- Neural networks for protein structure prediction -- Evolution teaches neural networks to predict protein structure -- An application of artificial neural networks in linguistics -- Optimization with neural networks -- Dynamics of networks and applications.
Neural-network models for event analysis are widely used in experimental high-energy physics, star/galaxy discrimination, control of adaptive optical systems, prediction of nuclear properties, fast interpolation of potential energy surfaces in chemistry, classification of mass spectra of organic compounds, protein-structure prediction, analysis of DNA sequences, and design of pharmaceuticals. This book, devoted to this highly interdisciplinary research area, addresses scientists and graduate students. The pedagogically written review articles range over a variety of fields including astronomy, nuclear physics, experimental particle physics, bioinformatics, linguistics, and information processing.
9783540489801
10.1007/BFb0104276 doi
Artificial intelligence.
Statistical physics.
Complex Systems.
Particle and Nuclear Physics.
Artificial Intelligence.
Statistical Physics and Dynamical Systems.
QC174.7-175.36
621
Neural networks: New tools for modelling and data analysis in science -- Adaptive optics: Neural network wavefront sensing, reconstruction, and prediction -- Nuclear physics with neural networks -- Using neural networks to learn energy corrections in hadronic calorimeters -- Neural networks for protein structure prediction -- Evolution teaches neural networks to predict protein structure -- An application of artificial neural networks in linguistics -- Optimization with neural networks -- Dynamics of networks and applications.
Neural-network models for event analysis are widely used in experimental high-energy physics, star/galaxy discrimination, control of adaptive optical systems, prediction of nuclear properties, fast interpolation of potential energy surfaces in chemistry, classification of mass spectra of organic compounds, protein-structure prediction, analysis of DNA sequences, and design of pharmaceuticals. This book, devoted to this highly interdisciplinary research area, addresses scientists and graduate students. The pedagogically written review articles range over a variety of fields including astronomy, nuclear physics, experimental particle physics, bioinformatics, linguistics, and information processing.
9783540489801
10.1007/BFb0104276 doi
Artificial intelligence.
Statistical physics.
Complex Systems.
Particle and Nuclear Physics.
Artificial Intelligence.
Statistical Physics and Dynamical Systems.
QC174.7-175.36
621