115 citations · 279 across the 14 of their papers we have counts for
14 papers
Simultaneous inference of environmental and interaction forces in collective dynamics
Nipuni de Silva, Ming Zhong, James M. Greene
Collective dynamics arise in a wide range of physical, biological, and engineering applications. Examples include cell migration, swarm robotics, social dynamics, and animal behavi…
The Emergence of Lines of Hierarchy in Collective Motion of Biological Systems
James Greene, Eitan Tadmor, Ming Zhong
The emergence of large scale structures in biological systems, and in particular the formation of lines of hierarchy, is observed in many scales, from collections of cells to group…
Using Physics Informed Neural Networks for Supernova Radiative Transfer Simulation
Xingzhuo Chen, David J. Jeffery, Ming Zhong +3
We use physics informed neural networks (PINNs) to solve the radiative transfer equation and calculate a synthetic spectrum for a Type Ia supernova (SN~Ia) SN 2011fe. The calculati…
Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator
Ming Zhong, Zhenya Yan
In this paper, we firstly extend the Fourier neural operator (FNO) to discovery the soliton mapping between two function spaces, where one is the fractional-order index space $\{ε|…
Learning Interaction Variables and Kernels from Observations of Agent-Based Systems
Jinchao Feng, Mauro Maggioni, Patrick Martin +1
Dynamical systems across many disciplines are modeled as interacting particles or agents, with interaction rules that depend on a very small number of variables (e.g. pairwise dist…
Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides
Ming Zhong, Jason Miller, Mauro Maggioni
Building accurate and predictive models of the underlying mechanisms of celestial motion has inspired fundamental developments in theoretical physics. Candidate theories seek to ex…
GEM: A General Evaluation Benchmark for Multimodal Tasks
Lin Su, Nan Duan, Edward Cui +7
In this paper, we present GEM as a General Evaluation benchmark for Multimodal tasks. Different from existing datasets such as GLUE, SuperGLUE, XGLUE and XTREME that mainly focus o…
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
Huaishao Luo, Lei Ji, Ming Zhong +4
Video-text retrieval plays an essential role in multi-modal research and has been widely used in many real-world web applications. The CLIP (Contrastive Language-Image Pre-training…
Dynamic Network Embedding Survey
Guotong Xue, Ming Zhong, Jianxin Li +3
Since many real world networks are evolving over time, such as social networks and user-item networks, there are increasing research efforts on dynamic network embedding in recent…
Learning Interaction Kernels for Agent Systems on Riemannian Manifolds
Mauro Maggioni, Jason Miller, Hongda Qiu +1
Interacting agent and particle systems are extensively used to model complex phenomena in science and engineering. We consider the problem of learning interaction kernels in these…
Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems
Jason Miller, Sui Tang, Ming Zhong +1
Modeling the complex interactions of systems of particles or agents is a fundamental scientific and mathematical problem that is studied in diverse fields, ranging from physics and…
Data-driven Discovery of Emergent Behaviors in Collective Dynamics
Mauro Maggioni, Jason Miller, Ming Zhong
Particle- and agent-based systems are a ubiquitous modeling tool in many disciplines. We consider the fundamental problem of inferring interaction kernels from observations of agen…
GDP: Generalized Device Placement for Dataflow Graphs
Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi +8
Runtime and scalability of large neural networks can be significantly affected by the placement of operations in their dataflow graphs on suitable devices. With increasingly comple…
Nonparametric inference of interaction laws in systems of agents from trajectory data
Fei Lu, Mauro Maggioni, Sui Tang +1
Inferring the laws of interaction between particles and agents in complex dynamical systems from observational data is a fundamental challenge in a wide variety of disciplines. We…