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Ming Zhong 0008
Person information
- affiliation: Texas A&M University, Institute of Data Science, College Station, TX, USA
- affiliation: Johns Hopkins University, Department of Applied Mathematics & Statistics, Baltimore, MD, USA
- affiliation (PhD 2016): University of Maryland, College Park, MD, USA
Other persons with the same name
- Ming Zhong — disambiguation page
- Ming Zhong 0001 — Tsinghua National Laboratory for Information Science and Technology, Beijing, China (and 1 more)
- Ming Zhong 0002
— Wuhan University, School of Computer Science, Wuhan, China
- Zhong Ming 0001
- Ming Zhong 0004
- Ming Zhong 0005
- Ming Zhong 0006 — Google, Inc., Mountain View, CA, USA (and 1 more)
- Ming Zhong 0007 — Google, Mountain View, CA, USA (and 1 more)
- Ming Zhong 0009
- Ming Zhong 0010
- Ming Zhong 0011
- Ming Zhong 0012
- Ming Zhong 0013 — York University, Toronto, Canada
- Ming Zhong 0014 — Microsoft, AI for Good Research Lab, USA
- Ming Zhong 0015 — Microsoft STCA, Beijing, China (and 1 more)
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2020 – today
- 2024
- [i11]Ming Zhong, Dehao Liu, Raymundo Arróyave, Ulisses Braga-Neto:
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes. CoRR abs/2404.05817 (2024) - [i10]Baoli Hao, Ulisses Braga-Neto, Chun Liu, Lifan Wang, Ming Zhong:
Structure Preserving PINN for Solving Time Dependent PDEs with Periodic Boundary. CoRR abs/2404.16189 (2024) - 2023
- [j2]Emilio Jose Rocha Coutinho
Physics-informed neural networks with adaptive localized artificial viscosity. J. Comput. Phys. 489: 112265 (2023) - [i9]Jinchao Feng, Ming Zhong:
Learning Collective Behaviors from Observation. CoRR abs/2311.00875 (2023) - 2022
- [i8]Emilio Jose Rocha Coutinho, Marcelo Dall'Aqua, Levi D. McClenny, Ming Zhong, Ulisses Braga-Neto, Eduardo Gildin:
Physics-Informed Neural Networks with Adaptive Localized Artificial Viscosity. CoRR abs/2203.08802 (2022) - [i7]Jinchao Feng, Mauro Maggioni, Patrick Martin, Ming Zhong:
Learning Interaction Variables and Kernels from Observations of Agent-Based Systems. CoRR abs/2208.02758 (2022) - 2021
- [c1]Mauro Maggioni, Jason Miller, Hongda Qiu, Ming Zhong:
Learning Interaction Kernels for Agent Systems on Riemannian Manifolds. ICML 2021: 7290-7300 - [i6]Mauro Maggioni, Jason Miller, Hongda Qiu, Ming Zhong:
Learning Interaction Kernels for Agent Systems on Riemannian Manifolds. CoRR abs/2102.00327 (2021) - [i5]Ming Zhong, Jason Miller, Mauro Maggioni:
Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides. CoRR abs/2108.11894 (2021) - 2020
- [i4]Eitan Tadmor, Ming Zhong
Sparse recovery of noisy data using the Lasso method. CoRR abs/2004.04348 (2020) - [i3]Jason Miller, Sui Tang
Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems. CoRR abs/2010.03729 (2020)
2010 – 2019
- 2019
- [i2]Mauro Maggioni, Jason Miller, Ming Zhong:
Data-driven Discovery of Emergent Behaviors in Collective Dynamics. CoRR abs/1912.11123 (2019) - 2018
- [i1]Fei Lu, Mauro Maggioni, Sui Tang, Ming Zhong:
Nonparametric inference of interaction laws in systems of agents from trajectory data. CoRR abs/1812.06003 (2018) - 2011
- [j1]Benjamin Ganis, Ivan Yotov, Ming Zhong
A Stochastic Mortar Mixed Finite Element Method for Flow in Porous Media with Multiple Rock Types. SIAM J. Sci. Comput. 33(3): 1439-1474 (2011)
Coauthor Index
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