Yusuke Nomura's Website
Invited Talks
2024
1. "Quantum many-body simulations using artificial neural networks"
Joint ICTP-WE Heraeus School and Conference on Frontiers at the Intersection of Quantum Simulation and Machine Learning
April 15, 2024, Trieste, Italy
2. "人工ニューラルネットワークを用いた量子多体問題の解析"
計算物理 春の学校 2024
March 15, 2024, Okinawa, Japan
2023
1. "量子多体問題と人工ニューラルネットワーク"
ipi - ダイキン シンポジウム
October 24, 2023, Tokyo, Japan
2. "Quantum many-body solvers using artificial neural networks"
Computational Approaches to Quantum Many-Body Systems
October 19, 2023, Saitama, Japan
3. "Doping Asymmetry and Layer-Selective Metal-Insulator Transition in Trilayer K3C60"
The Asia-Pacific Workshop on Strongly Correlated Systems 2023
September 23, 2023, Beijing, China
4. "人工ニューラルネットワークを用いた強相関電子系の解析"
研究会「強相関電子系のフロンティア」
August 22, 2023, Nagoya, Japan
5. "Artificial neural networks for analyzing quantum many-body correlations"
Conference of Condensed Matter Physics (CCMP) 2023
August 8, 2023, Liyang, China
6. "Quantum many-body problems and artificial neural networks"
CCP2023 - 34th IUPAP Conference on Computational Physics
August 5, 2023, Kobe, Japan
7. "ニッケル酸化物超伝導体の電子構造と物質デザイン"
金研研究会「強相関物質における創発物性研究の現状と将来展望」
April 22, 2023, Sendai, Japan
8. "量子多体物理と人工ニューラルネットワーク"
第2回量子ソフトウェアワークショップ
January 30, 2023, Tokyo, Japan
2022
1. "銅酸化物の類似物質としてのd9ニッケル酸化物の理論デザイン"
第32回日本MRS年次大会
December 6, 2022, Yokohama, Japan
2. "機械学習手法を用いた量子多体系の研究"
第16回物性科学領域横断研究会
November 25, 2022 (online)
第17回凝縮系科学賞 記念講演
3. "物理学者、機械学習を使う ー量子多体系への適用"
光量子科学セミナー
August 29, 2022, Yokohama, Japan
4. "Artificial neural networks for exploring quantum many-body physics"
Variational Learning for Quantum Matter
July 4, 2022, Lausanne, Switzerland
5. "Symmetry-adapted Wannier functions"
Wannier 2022 Summer School
May 20, 2022 (online)
6. "Nickelate superconductors: electronic and magnetic properties and materials design"
日本材料科学会 2022年度学術講演大会
May 19, 2022, Yokohama, Japan
7. "Artificial neural networks for quantum many-body problems"
APS March Meeting 2022
March 16, 2022 (online)
2021
1. "フラーレン超伝導体薄膜における超伝導のドーピング非対称性"
京都大学基礎物理学研究所研究会 「非自明な電子状態が生み出す超伝導現象の最前線:新たな挑戦と展望」
December 24, 2021, Kyoto, Japan
2. "Artificial Neural Networks for Analyzing Quantum Many-Body Problems"
KMS 2021 Winter Conference
November 25, 2021 (online)
3. "Artificial neural networks for representing quantum many-body states"
Quantum Techniques in Machine Learning (QTML) 2021
November 11, 2021 (online)
4. "Materials design of cuprate-analog nickelates and magnetic exchange coupling "
2nd International Meeting on Thin Film Interfaces, Surfaces and Composite Crystals
November 9, 2021 (online)
5. "Purifying deep Boltzmann machines for thermal quantum states"
APW-RIKEN-Tsinghua-Kavli workshop “Highlights on condensed matter physics” 2021
October 23, 2021 (online)
6. "Quantum many-body problems and artificial neural networks"
Statphys seminar
October 18, 2021 (online)
7. "量子多体問題とニューラル・ネットワーク"
統計物理と統計科学のセミナー
May 18, 2021 (online)
8. "Materials design of layered d9 nickelates and magnetic exchange coupling"
Quantum Liquid Crystals 2021 (QLC2021)
May 11, 2021 (online)
2020
1. "機械学習を用いた2次元J1-J2ハイゼンベルグ模型の研究"
物性研究所スパコン共同利用・CCMS合同研究会「計算物質科学の新展開2020」
December 21, 2020 (online)
2. "ニッケル酸化物新超伝導体における強相関電子系形成と物質デザイン"
京都大学基礎物理学研究所研究会「高温超伝導・非従来型超伝導研究の最前線：多様性と普遍性」
October 27, 2020 (online)
3. "ニッケル酸化物超伝導体NdNiO2における強相関電子系形成と新物質デザイン"
JPS Autumn Meeting 2020
September 8, 2020 (online)
4. "機械学習手法を用いた量子多体系の研究 ー手法拡張を含む最近の進展"
Online CMT seminars
August 24, 2020 (online)
5. "ボルツマンマシンを用いた量子多体波動関数表現：深層ボルツマンマシンによる厳密な表現と制限ボルツマンマシンによる数値的近似表現"
Deep learning and Physics 2020
August 6, 2020 (online)
6. "Symmetry-adapted Wannier functions"
School on Wannier90 v3.0: new features and applications
March 26, 2020 (online)
7. "Machine learning for analyzing strongly-correlated systems"
International Workshop on High-Performance Computing and Programming on Quantum Chemistry and Physics 2020 (HPCPQCP2020)
January 16, 2020, Kobe, Japan
2019
1. "Machine learning solvers for strongly-correlated systems"
Computational Approaches to Quantum Many-body Problems (CAQMP2019)
July 29, 2019, Kashiwa, Japan
2. "Machine learning for studying strongly correlated systems"
The 5th Conference on Condensed Matter Physics (CCMP)
June 28, 2019, Liyang, China
3. "物性物理における機械学習"
パリ異分野融合科学者の会 第1回研究会
June 15, 2019, Tokyo, Japan
4. "深層ボルツマンマシンを用いた量子多体系の基底状態の厳密な構築"
第八回「強相関電子系理論の最前線」
February 4, 2019, Atami, Japan
2018
1. "Solving quantum many-body Hamiltonians with artificial neural networks"
Tensor Network States: Algorithms and Applications (TNSAA) 2018-2019
December 3, 2018, Kobe, Japan
2. "Machine-learning solvers for quantum many-body systems"
JPS Autumn Meeting 2018
September 10, 2018, Kyotanabe, Japan
3. "Machine learning using Boltzmann machines for solving strongly correlated systems"
Computational Approaches to Magnetic Systems (CAMS 2018)
August 27, 2018, Pohang, Korea
4. "機械学習を用いた量子多体系の基底状態計算"
物性研究所スパコン共同利用・CCMS合同研究会「計算物質科学の今と未来」
April 3, 2018, Kashiwa, Japan
5. "強相関系の数値的研究: フラーレン超伝導体の研究・機械学習の技術を使った多体系ソルバー"
第38回kyutechセミナー・応用物理学会特別講演会
March 30, 2018, Kitakyushu, Japan
2017
1. "制限付きボルツマンマシンを用いた量子多体系の機械学習ソルバーの開発"
"Machine-learning solver using restricted Boltzmann machine for quantum many-body systems"
第七回「強相関電子系理論の最前線」
December 4, 2017, Hakodate, Japan
2. "Enhancing superconductivity in A3C60 fullerides"
JPS Annual (72th) meeting
March 20, 2017, Osaka, Japan
3. "アルカリ金属をドープしたフラーレン化合物における高温s波超伝導の第一原理的研究"
日本物理学会 第72回年次大会
March 18, 2017, Osaka, Japan
4. "Review on first-principles calculations using DMFT"
動的平均場近似計算に関する情報交流会(ワークショップ)
February 17, 2017, Tokyo, Japan
5. "Enhancing superconductivity in alkali-doped fullerides"
CEMS-QPEC Topical Meeting on Superconductivity under Extreme Conditions
January 17, 2017, Tokyo, Japan
2016
1. "Exotic high-Tc s-wave superconductivity in alkali-doped fullerides"
The New Generation in Strongly Correlated Electrons Systems (NGSCES) 2016
September 27, 2016, Trieste, Italy
2. "Exotic high-Tc s-wave superconductivity in A3C60 (A = K, Rb, Cs)"
Spectroscopies in Novel Superconductors (SNS) 2016
June 23, 2016, Ludwigsburg, Germany