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Applying AI to Quantum Field Theory (Applying AI to Science) - Softcover
Book 1 of 3: Applying AI to Science
Synopsis
What happens when you teach neural networks the deepest symmetries of nature?
This book is a hands-on introduction to one of the most exciting frontiers in science: the convergence of artificial intelligence and quantum field theory. Written for physicists curious about machine learning and for AI practitioners drawn to fundamental physics, it bridges both worlds with clarity, rigor, and working Python code.
The journey begins with a surprising discovery. The renormalization group, one of the most powerful tools in theoretical physics, maps directly onto the information flow through neural network layers. Gauge symmetry, the principle that governs every fundamental force, provides architectural blueprints for AI systems. Readers build a neural network from scratch that identifies phase transitions without being taught any physics, demonstrating how AI can rediscover fundamental principles from raw data alone.
The book then examines how AI tackles each type of quantum field. Neural networks reveal exotic scalar field phases that traditional methods miss. DeepMind's FermiNet achieves chemical accuracy for molecules with up to 30 electrons. MIT's gauge-equivariant normalizing flows reduce lattice QCD autocorrelation times by a factor of 100, conquering the critical slowing down that has stalled simulations for decades. Transformers compress million-term scattering amplitudes into single equations.
The final chapters look ahead to AI systems that do not merely calculate but create. Systems like MELVIN design quantum experiments no human has imagined. Language models solve bootstrap equations. Neural networks propose pathways toward grand unification. The book closes with the emerging partnership between quantum computers and classical AI, a combination that may finally unlock QFT's deepest unsolved problems.
Includes 17 chapters, a glossary, working code examples, and a companion GitHub repository.
"synopsis" may belong to another edition of this title.
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Applying AI to Quantum Field Theory (Paperback)
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Paperback. Condition: new. Paperback. What happens when you teach neural networks the deepest symmetries of nature?This book is a hands-on introduction to one of the most exciting frontiers in science: the convergence of artificial intelligence and quantum field theory. Written for physicists curious about machine learning and for AI practitioners drawn to fundamental physics, it bridges both worlds with clarity, rigor, and working Python code.The journey begins with a surprising discovery. The renormalization group, one of the most powerful tools in theoretical physics, maps directly onto the information flow through neural network layers. Gauge symmetry, the principle that governs every fundamental force, provides architectural blueprints for AI systems. Readers build a neural network from scratch that identifies phase transitions without being taught any physics, demonstrating how AI can rediscover fundamental principles from raw data alone.The book then examines how AI tackles each type of quantum field. Neural networks reveal exotic scalar field phases that traditional methods miss. DeepMind's FermiNet achieves chemical accuracy for molecules with up to 30 electrons. MIT's gauge-equivariant normalizing flows reduce lattice QCD autocorrelation times by a factor of 100, conquering the critical slowing down that has stalled simulations for decades. Transformers compress million-term scattering amplitudes into single equations.The final chapters look ahead to AI systems that do not merely calculate but create. Systems like MELVIN design quantum experiments no human has imagined. Language models solve bootstrap equations. Neural networks propose pathways toward grand unification. The book closes with the emerging partnership between quantum computers and classical AI, a combination that may finally unlock QFT's deepest unsolved problems.Includes 17 chapters, a glossary, working code examples, and a companion GitHub repository. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798298713795
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Applying AI to Quantum Field Theory (Paperback)
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Paperback. Condition: new. Paperback. In 2019, Giuseppe Carleo and his team pioneered the application of machine learning to quantum physics. This book is an introduction to how AI revolutionizes quantum field theory (QFT), from scalar fields to complex gauge theories describing quarks and gluons. The narrative unfolds in three acts. First, readers discover the mathematical kinship between neural networks and quantum fields-the renormalization group maps onto information flow through neural layers, while gauge symmetry provides blueprints for AI architectures. Through Python code, readers build networks that discover phase transitions without being taught physics, demonstrating AI's ability to rediscover fundamental principles from data alone. The second act examines how AI addresses each type of quantum field. For scalar fields, neural networks identify exotic phases that traditional methods miss. For fermions, architectures like FermiNet achieve chemical accuracy while sidestepping computational barriers. For gauge fields, flow-based models conquer critical slowing down that has limited simulations for decades. Key breakthroughs include MIT's gauge-equivariant flows, which reduce autocorrelation times by a factor of 100, DeepMind's solution to 30-electron molecules, and the discovery by transformers that million-term scattering amplitudes can be expressed as a single equation. The final act envisions AI not just calculating but creating physics systems like MELVIN, designing quantum experiments that no human has imagined. Language models solve bootstrap equations. Neural networks propose routes to grand unification. The book culminates in a convergence of quantum computers and classical AI-a partnership that could crack QFT's deepest mysteries. By teaching AI nature's symmetries, we're creating systems that reveal patterns invisible to human analysis-AI intelligence is offering a different way of interrogating reality. Written as an introduction for physicists curious about AI and ML, as well as for AI and ML experts interested in fundamental physics, the book strikes a balance between rigor and practical implementation, offering both conceptual frameworks and tools for the quantum field theory revolution. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798298713795
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