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Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles.

, , and . Int. J. High Perform. Comput. Appl., (2020)

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SciSpot: Scientific Computing On Temporally Constrained Cloud Preemptible VMs., , and . IEEE Trans. Parallel Distributed Syst., 33 (12): 3575-3588 (2022)Designing Machine Learning Surrogates using Outputs of Molecular Dynamics Simulations as Soft Labels., , and . CoRR, (2021)Modeling The Temporally Constrained Preemptions of Transient Cloud VMs., , and . HPDC, page 41-52. ACM, (2020)Machine Learning for Performance Enhancement of Molecular Dynamics Simulations., , and . ICCS (2), volume 11537 of Lecture Notes in Computer Science, page 116-130. Springer, (2019)Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms., and . CLOUD, page 751-753. IEEE, (2021)Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning., and . EduHPC@SC, page 25-34. IEEE, (2020)Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles., , and . Int. J. High Perform. Comput. Appl., (2020)Modeling Constrained Preemption Dynamics Of Transient Cloud Servers., , and . CoRR, (2019)Solving Newton's equations of motion with large timesteps using recurrent neural networks based operators., , and . Mach. Learn. Sci. Technol., 3 (2): 25002 (2022)Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation., , , , , , , , , and 3 other author(s). IPDPS Workshops, page 422-429. IEEE, (2019)