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Artificial Intelligence for Science: A Deep Learning Revolution
This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.
Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.
The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.
Researchers, professionals, academics, and graduate students in AI/machine learning, neural networks, data science, and science and engineering. The latter areas include astronomy, climate, energy, environment, health, life sciences, materials, particle physics, looking at theory, observation, and the design, construction, and control of experimental facilities.
Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.
The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.
Researchers, professionals, academics, and graduate students in AI/machine learning, neural networks, data science, and science and engineering. The latter areas include astronomy, climate, energy, environment, health, life sciences, materials, particle physics, looking at theory, observation, and the design, construction, and control of experimental facilities.
802 pages, Kindle Edition
Published March 21, 2023
4 people want to read
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