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RESEARCH PAPER · Physical review. E · 2018

Machine-learning quantum mechanics: Solving quantum mechanics problems using radial basis function networks

Peiyuan Teng

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33 direct citing papers in this saved network. Explore the next layer with second-order citations.

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42 direct citing works

Machine-learning quantum mechanics: Solving quantum mechanics problems using radial basis function networksOriginalNonequilibrium fluctuations of a driven quantum heat engine via machine learning9From DFT to machine learning: recent approaches to materials science–a review849Restricted Boltzmann machines in quantum physics200Sampling scheme for neuromorphic simulation of entangled quantum systems10Machine learning and the physical sciences2417Introduction0Phase Diagram Reconstruction of the Bose–Hubbard Model with a Restricted Boltzmann Machine Wavefunction9Data classification by quantum radial-basis-function networks16Approximating Ground State Energies and Wave Functions of Physical Systems with Neural Networks1Neural-network-based multistate solver for a static Schrödinger equation29A neural network approach for solution of the Schrödinger equation for a particle in the Pöschl–Teller potential3RBF-POD reduced-order modeling of flow field in the curved shock compression inlet45Train RBF networks with a hybrid genetic algorithm10Differential radial basis function network for sequence modelling0A Two-Phase Evolutionary Method to Train RBF Networks3RbfDeSolver: A Software Tool to Approximate Differential Equations Using Radial Basis Functions0Machine learning for vibrational spectroscopy2Machine learning for mechanics prediction of 2D MXene-based aerogels48Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method7Confined hydrogen atom: endohedrals H@C36 and H@C607Multiple linear regression and artificial neural networks for highly selective cationic β‐diimine‐methallyl nickel (II) catalyst for styrene dimerization reaction to 1,3‐diphenyl‐1‐butene31Stochastic representation of many-body quantum states14Adapt the Parameters of RBF Networks Using Grammatical Evolution1Adapting the Parameters of RBF Networks Using Grammatical Evolution1Modeling of the biosorption of Fe(III) ions by olive‐stone activated carbon28AliAmvra—Enhancing Customer Experience through the Application of Machine Learning Techniques for Survey Data Assessment and Analysis5Machine learning models for modeling the biosorption of Fe(III) ions by activated carbon from olive stone12A Hybrid Extended Kalman Filter Based on Parametrized ANNs for the Improvement of the Forecasts of Numerical Weather and Wave Prediction Models4From architectures to applications: a review of neural quantum states59A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models0A Modified Kalman Filter Based on Radial Basis Function Neural Networks for the Improvement of Numerical Weather Prediction Models2Application Exploration of Flow Field Prediction in Free Jet Tests Based on Proper Orthogonal Decomposition Method0Solving the vibrational Schrödinger equation with artificial neural networks3

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  1. Nonequilibrium fluctuations of a driven quantum heat engine via machine learning

    Physical review. E · 2019-02-01

    Sajal Kumar Giri, Himangshu Prabal Goswami

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  2. From DFT to machine learning: recent approaches to materials science–a review

    Journal of Physics Materials · 2019-02-19

    Gabriel Ravanhani Schleder, A. C. M. Padilha, Carlos Mera Acosta, Marcio Costa, A. Fazzio

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  3. Restricted Boltzmann machines in quantum physics

    Nature Physics · 2019-06-24

    Roger G. Melko, Giuseppe Carleo, Juan Carrasquilla, J. I. Cirac

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  4. Sampling scheme for neuromorphic simulation of entangled quantum systems

    Physical review. B./Physical review. B · 2019-11-13

    Stefanie Czischek, Jan Martin Pawlowski, Thomas Gasenzer, Martin Gärttner

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  5. Machine learning and the physical sciences

    Reviews of Modern Physics · 2019-12-06

    Giuseppe Carleo, J. I. Cirac, Kyle Stuart Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, Lenka Zdeborová

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  6. Introduction

    Springer theses · 2020-01-01

    Stefanie Czischek

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  7. Phase Diagram Reconstruction of the Bose–Hubbard Model with a Restricted Boltzmann Machine Wavefunction

    Journal of the Physical Society of Japan · 2020-07-28

    Vladimir Vargas-Calderón, Herbert Vinck-Posada, Fabio A. González

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  8. Data classification by quantum radial-basis-function networks

    Physical Review A · 2020-10-30

    Changpeng Shao

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  9. Approximating Ground State Energies and Wave Functions of Physical Systems with Neural Networks

    arXiv (Cornell University) · 2020-11-21

    Lema, Cesar, Anna Choromanska

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  10. Neural-network-based multistate solver for a static Schrödinger equation

    Physical Review A · 2021-03-08

    Hong Li, Qilong Zhai, Jeff Z. Y. Chen

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  11. A neural network approach for solution of the Schrödinger equation for a particle in the Pöschl–Teller potential

    Canadian Journal of Physics · 2021-03-16

    Garvit Arora, Apoorva Singh, Ashutosh Vishwa Bandhu

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  12. RBF-POD reduced-order modeling of flow field in the curved shock compression inlet

    Acta Astronautica · 2021-05-01

    Fei Sun, Wei-Yi Su, Mou-Yuan Wang, Renjie Wang

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  13. Train RBF networks with a hybrid genetic algorithm

    Evolutionary Intelligence · 2021-08-13

    Ioannis G. Tsoulos, Nikolaos Anastasopoulos, Georgios Ntritsos, Alexandros T. Tzallas

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  14. Differential radial basis function network for sequence modelling

    Expert Systems with Applications · 2021-10-14

    Kojo Sarfo Gyamfi, James Brusey, Elena I. Gaura

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  15. A Two-Phase Evolutionary Method to Train RBF Networks

    Applied Sciences · 2022-02-25

    Ioannis G. Tsoulos, Alexandros T. Tzallas, Evaggelos C. Karvounis

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  16. RbfDeSolver: A Software Tool to Approximate Differential Equations Using Radial Basis Functions

    Axioms · 2022-06-16

    Ioannis G. Tsoulos, Alexandros T. Tzallas, Evaggelos C. Karvounis

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  17. Machine learning for vibrational spectroscopy

    Elsevier eBooks · 2022-09-23

    Sergei Manzhos, Sergei Manzhos, Tucker Carrington

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  18. Machine learning for mechanics prediction of 2D MXene-based aerogels

    Composites Communications · 2022-12-25

    Chao Rong, Lei Zhou, Bowei Zhang, Fu‐Zhen Xuan

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  19. Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method

    Algorithms · 2023-01-21

    Ioannis G. Tsoulos, Vasileios Charilogis

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  20. Confined hydrogen atom: endohedrals H@C36 and H@C60

    Machine Learning Science and Technology · 2023-02-03

    Horacio Olivares‐Pilón, A. M. Escobar-Ruiz, Mario Alan Quiroz-Juarez, N. Aquino

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  21. Multiple linear regression and artificial neural networks for highly selective cationic β‐diimine‐methallyl nickel (II) catalyst for styrene dimerization reaction to 1,3‐diphenyl‐1‐butene

    Applied Organometallic Chemistry · 2023-06-06

    Kamel Landolsi, Fraj Echouchene, Abdullah Bajahzar, Hafedh Belmabrouk, Moncef Msaddek

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  22. Stochastic representation of many-body quantum states

    Nature Communications · 2023-06-16

    Hristiana Atanasova, Liam Bernheimer, Guy Cohen

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  23. Adapt the Parameters of RBF Networks Using Grammatical Evolution

    Preprints.org · 2023-09-22

    Ioannis G. Tsoulos, Alexandros T. Tzallas, Evaggelos C. Karvounis

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  24. Adapting the Parameters of RBF Networks Using Grammatical Evolution

    AI · 2023-12-11

    Ioannis G. Tsoulos, Alexandros T. Tzallas, Evaggelos C. Karvounis

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  25. Modeling of the biosorption of Fe(III) ions by olive‐stone activated carbon

    Applied Organometallic Chemistry · 2024-02-03

    Ayman Massaoudi, Fraj Echouchene, Mossaad Ben Ayed, Abdelwahed Berguiga, Ahlem Harchay, Hafedh Belmabrouk

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  26. AliAmvra—Enhancing Customer Experience through the Application of Machine Learning Techniques for Survey Data Assessment and Analysis

    Information · 2024-02-04

    Dimitrios Mpouziotas, Jeries Besharat, Ioannis G. Tsoulos, Chrysostomos D. Stylios

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  27. Machine learning models for modeling the biosorption of Fe(III) ions by activated carbon from olive stone

    Neural Computing and Applications · 2024-05-07

    Ayman Massaoudi, Fraj Echouchene, Mossaad Ben Ayed, Abdelwahed Berguiga, Ahlem Harchay, S. M. Al-Ghamdi, Hafedh Belmabrouk

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  28. A Hybrid Extended Kalman Filter Based on Parametrized ANNs for the Improvement of the Forecasts of Numerical Weather and Wave Prediction Models

    Atmosphere · 2024-07-10

    Athanasios Donas, George N. Galanis, Ioannis Pytharoulis, Ioannis Th. Famelis

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  29. From architectures to applications: a review of neural quantum states

    Quantum Science and Technology · 2024-08-22

    Hannah Lange, A. Van de Walle, Atiye Abedinnia, Annabelle Bohrdt

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  30. A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models

    Sensors · 2024-12-15

    Athanasios Donas, Ioannis Kordatos, Alex Alexandridis, George N. Galanis, Ioannis Th. Famelis

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  31. A Modified Kalman Filter Based on Radial Basis Function Neural Networks for the Improvement of Numerical Weather Prediction Models

    Atmosphere · 2025-02-22

    Athanasios Donas, George N. Galanis, Ioannis Pytharoulis, Ioannis Th. Famelis

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  32. Application Exploration of Flow Field Prediction in Free Jet Tests Based on Proper Orthogonal Decomposition Method

    Applied Sciences · 2025-12-25

    J WANG, Weiyi Su, Zhiyou Liu, Huijun Tan, You Zhang, Kaigang Guan, Qin Shu

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  33. Solving the vibrational Schrödinger equation with artificial neural networks

    Nature Communications · 2026-06-20

    Shuaishuai Zhao, Dong Hui Zhang

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