MLPhys Foundation of “Machine Learning Physics” Grant-in-Aid for Transformative Research Areas (A) MLPhys Foundation of “Machine Learning Physics” Grant-in-Aid for Transformative Research Areas (A)

Overview

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Head Investigator

Koji Hashimoto

Professor
Particle Physics Theory Group
Department of physics, Kyoto University

The research area "Machine Learning Physics" will begin
with the aim of discovering new laws and pioneering new materials

Hello. My name is Koji Hashimoto, Professor of Graduate School of Science, Kyoto University. Let me explain about the "Learning Physics Domain" that we are just now trying to create. This new transformative research area aims to revolutionize fundamental physics by combining machine learning and physics.

Throughout its long history, physics has provided the most precise testing ground in the natural sciences, solving problems in various natural hierarchies in collaboration with the mathematical sciences.

On the other hand, the field of machine learning is a major research field, a mathematical system that forms the foundation of artificial intelligence and has seen explosive progress in recent years due to advances in computational science. We are launching the transformative research area "Machine Learning Physics" to integrate these two major fields.

In this area, we will tackle the most important challenges in fundamental physics, such as the discovery of new laws and the exploration of new materials, by integrating machine learning methods with physics.

Although the fields of physics in which machine learning is used are diverse, we have structured the core of our research area to focus on subjects where theoretical fusion is possible. We prepare physics fields for fusion (computational physics, particle physics, condensed matter physics, quantum and gravitational physics) and machine learning methods (mathematical, high-dimensional statistics, topology) and promote fusion research from the intersection of each to create the machine learning physics area. These planned research projects will be supplemented by open research proposals, aiming to create a wide range of fusion research areas.

We look forward to your participation.

Overview of research in our area
research summary

Physics:The most precise testing ground in natural science Multi-hierarchical problems and collaborative mathematics. Machine Learning; Explosive field of computational science Social and technological innovation Physics・Machine Learning

Machine Learning Physics

Discovering new laws, pioneering new materials

Solving fundamental problems in physics by integrating theoretical methods in machine learning and in physics

Structure of this area dedicated to fusion purposes

Create intersections in selected physics fields and machine learning methods to promote intersection fusion research and create a common new fundamental area.

Management group: Koji Hashimoto (Head investigator),A01: Akio Tomiya (IPUT Osaka) : Computational physics,A02: Mhoko Nojiri (KEK) : High Energy Physics,A03: Tomi Ohtsuki (Sphia U) : Condensed Matter Physics,A04: Koji Hashimoto (Kyoto U) : Quantum and Gravity Physics,B01: Akinori Tanaka (RIKEN AIP) : Math and Application of DL,B02: Yoshiyuki Kabashima (U. Tokyo) : Statistical data ML,B03: Kenji Fukushima (U. Tokyo) : Topology and Geometry of ML Management group: Koji Hashimoto (Head investigator),A01: Akio Tomiya (IPUT Osaka) : Computational physics,A02: Mhoko Nojiri (KEK) : High Energy Physics,A03: Tomi Ohtsuki (Sphia U) : Condensed Matter Physics,A04: Koji Hashimoto (Kyoto U) : Quantum and Gravity Physics,B01: Akinori Tanaka (RIKEN AIP) : Math and Application of DL,B02: Yoshiyuki Kabashima (U. Tokyo) : Statistical data ML,B03: Kenji Fukushima (U. Tokyo) : Topology and Geometry of ML

Target of research

GoalA
By integrating physics and machine learning,
we solve fundamental problems in physics
A01Innovative acceleration of quantum configuration generation in computational physics.
A02 Improvement of the sensitivity of large accelerator experiments and refinement of corresponding theories in particle physics.
A03Extension to quantum complex regime in condensed matter physics/ Elucidation of quantum fluctuation and quantum entanglement.
A04Elucidation of the emergence mechanism of the space-time concept in quantum and gravitational physics.
GoalB
Develop methods for solving problems
in physics through our new area that takes advantage of the affinity between machine learning and physics.
B01Use domain knowledge from physics to mathematically elucidate the mechanisms of deep learning and classify methods to deal with problems.
B02Overcome the problem of computational difficulty in learning by statistical mechanics/ Develop a framework that runs through theory and practice.
B03Develop a physics-friendly neural network learning method using topological geometry.
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