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Sawado Lab
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A theoretical physics lab that explores particle physics, cosmology, and condensed matter through topological solitons.
AboutAbout the lab
Understanding physics in a unified way through the study of solitons.
The Sawado Lab uses topological solitons as a key to studying phenomena in particle physics, cosmology, and condensed matter.
Topological solitons are a concept used to describe a wide range of phenomena, including the properties of elementary particles, black holes and braneworlds, and the properties of superconductors and semimetals.
The lab investigates these using analytical methods and numerical simulations.
Key terms
- Topological soliton
- A concept used to describe a wide range of phenomena, including the properties of elementary particles, astrophysics, and the properties of superconductors and semimetals.
- Braneworld
- Also called a membrane universe, a concept used in astrophysics. The lab works on new constructions of it using soliton solutions.
- Physics Informed Neural Networks
- A deep learning method based on physical data. The lab aims to refine it and improve its performance.
ResearchResearch themes
Through the study of soliton solutions, which are particular solutions of certain nonlinear differential equations, the lab aims to understand a wide range of problems, from condensed matter physics to particle and nuclear physics and cosmology, in a unified way. It is also working on refining and improving the performance of deep learning based on physical data.
*Illustrative imageNumerical Analysis of Soliton Solutions
The lab studies soliton solutions such as instantons, monopoles, and skyrmions through numerical analysis.
*Illustrative imageDeriving and Applying Unknown Solutions
The lab derives solutions that are not yet known, such as knot solutions and vortex-line solutions, and considers their applications to physics.
*Illustrative imageNew Braneworlds Built from Solitons
The lab uses soliton solutions to construct new braneworlds (membrane universes) and aims to solve various hierarchy problems.
*Illustrative imageDeep Learning Based on Physical Data
The lab aims to refine and improve the performance of deep learning based on physical data (Physics Informed Neural Networks), and to obtain answers to the problems in its research themes from a new perspective.
MembersFaculty
- ProfessorNobuyuki Sawado
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Neel skyrmion models” Börge Göbel, Ingrid Mertig, Oleg A.Tretiakov / CC BY 4.0 (cropped)
- Numerical Analysis of Soliton Solutions: “Soliton hydro” Christophe.Finot et Kamal HAMMANI / CC BY-SA 2.5 (cropped)
- Deriving and Applying Unknown Solutions: “Equation for a heart” Luka Maglc / CC0 (cropped)
- New Braneworlds Built from Solitons: “NASA-HS201427a-HubbleUltraDeepField” NASA, ESA, H. Teplitz and M. Rafelski (IPAC/Caltech), A. Koekemoer (STScI), R. Windhorst (Arizona State University), and Z. Levay (STScI) / Public domain (cropped)
- Deep Learning Based on Physical Data: “Deep BSDE Method” AzzurroLan / CC BY-SA 4.0 (cropped)
- Related lab (石塚研究室): “CHIPS-M neutrino detector ( )” UCL Mathematical and Physical Sciences from London, UK / CC BY 2.0 (cropped)
- Related lab (鈴木研究室): “Neutron Star X-ray beaming with accretion disk” NASA / Public domain (cropped)
- Related lab (阿部研究室): “Zeus jul08” Luis Sanchez (talk) / CC BY-SA 3.0 (cropped)
- Related lab (吉岡研究室): “Morpho peleides Niagara Parks Butterfly Conservatory” The Cosmonaut / CC BY-SA 2.5 ca (cropped)
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