About this Research Topic
The bridge between AI and interdisciplinary physics is both a two-way street and also at a crossroads; that is, insights and methods from physics are being used to advance AI. Methods and concepts from statistical physics are widely used to design algorithms for inference, sampling, and optimization, as well as to better understand why deep learning works so well. At the same time, neuronal, optical and quantum mechanical systems provide unique physical substrates that can be harnessed to design and implement diverse forms of learning and computation.
These important theoretical and methodological challenges of AI lie at the interface between several disciplines, including computer science, mathematics and physical sciences. Thus, we propose an interdisciplinary collaboration to collect, integrate, and synthesize the different results and perspectives in the related fields.
Hence, this Research Topic focuses on the theory, methods, and application of machine learning and artificial intelligence, as well as their application to physical science disciplines as a whole. Accordingly, we welcome contributions that:
1) provide theoretical understanding or insights about machine learning algorithms;
2) illustrate an application of machine learning or AI to model, simulate, or analyze data from a complex system;
3) propose novel implementations or applications of different substrates for learning and computation.
Please note that submissions through Frontiers in Artificial Intelligence must make at least some reference to both Artificial Intelligence and Machine Learning.
Keywords: deep learning, pattern recognition, complex systems, machine learning, computational physics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.