Machine Learning and Data-Driven Approaches in Nuclear and Quantum Physics
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E4: Mathematical Physics".
Deadline for manuscript submissions: 15 March 2027 | Viewed by 100
Editors
2. Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA 70803-4001, USA
Interests: quantum mechanics; geometry; pure mathematics; mathematical modelling
Special Issues, Collections and Topics in MDPI journals
Interests: interacting boson model; solvable models; nuclear structure; band mixing; Hartree Fock method; density functional theory; artificial intelligence; machine learning; neural networks
Special Issue Information
Dear Colleagues,
This Special Issue focuses on recent advances in machine learning, artificial intelligence (AI), and data-driven methodologies in nuclear and quantum physics. Modern AI techniques are rapidly transforming theoretical, computational, and experimental research by enabling efficient analysis of complex many-body systems, accelerating large-scale simulations, improving uncertainty quantification, and uncovering hidden patterns in high-dimensional physical data. At the same time, the integration of machine learning with quantum computing, quantum information science, and physics-informed modelling is opening new opportunities for addressing challenging problems in nuclear structure, quantum dynamics, strongly correlated systems, and quantum control. We welcome contributions on novel theoretical developments, computational algorithms, and practical applications of AI and machine learning in nuclear and quantum physics. Particular emphasis is placed on interpretable and physics-informed approaches, quantum machine learning, surrogate modelling, reinforcement learning, and hybrid AI–physics frameworks for scientific discovery.
Dr. Feng Pan
Dr. Amir Jalili
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- artificial intelligence
- nuclear physics
- quantum physics
- quantum machine learning
- physics-informed neural networks
- uncertainty quantification
- quantum computing
- data-driven modelling
- neural network
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