135 citations · 298 across the 12 of their papers we have counts for
13 papers
Improving Gradient Computation for Differentiable Physics Simulation with Contacts
Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey +1
Differentiable simulation enables gradients to be back-propagated through physics simulations. In this way, one can learn the dynamics and properties of a physics system by gradien…
A Neural ODE Interpretation of Transformer Layers
Yaofeng Desmond Zhong, Tongtao Zhang, Amit Chakraborty +1
Transformer layers, which use an alternating pattern of multi-head attention and multi-layer perceptron (MLP) layers, provide an effective tool for a variety of machine learning pr…
Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control?
Yaofeng Desmond Zhong, Jiequn Han, Georgia Olympia Brikis
In recent years, an increasing amount of work has focused on differentiable physics simulation and has produced a set of open source projects such as Tiny Differentiable Simulator,…
EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System
Haoran Su, Yaofeng D. Zhong, Joseph Y. J. Chow +2
Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch…
A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
Haoran Su, Yaofeng Desmond Zhong, Biswadip Dey +1
Emergency vehicles (EMVs) play a critical role in a city's response to time-critical events such as medical emergencies and fire outbreaks. The existing approaches to reduce EMV tr…
EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
Haoran Su, Yaofeng Desmond Zhong, Biswadip Dey +1
Emergency vehicles (EMVs) play a crucial role in responding to time-critical events such as medical emergencies and fire outbreaks in an urban area. The less time EMVs spend travel…
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models
Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
The incorporation of appropriate inductive bias plays a critical role in learning dynamics from data. A growing body of work has been exploring ways to enforce energy conservation…
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data
Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
The last few years have witnessed an increased interest in incorporating physics-informed inductive bias in deep learning frameworks. In particular, a growing volume of literature…
Influence Spread in the Heterogeneous Multiplex Linear Threshold Model
Yaofeng Desmond Zhong, Vaibhav Srivastava, Naomi Ehrich Leonard
The linear threshold model (LTM) has been used to study spread on single-layer networks defined by one inter-agent sensing modality and agents homogeneous in protocol. We define an…
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control
Yaofeng Desmond Zhong, Naomi Ehrich Leonard
Recent approaches for modelling dynamics of physical systems with neural networks enforce Lagrangian or Hamiltonian structure to improve prediction and generalization. However, whe…
Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning
Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
In this work, we introduce Dissipative SymODEN, a deep learning architecture which can infer the dynamics of a physical system with dissipation from observed state trajectories. To…
A Continuous Threshold Model of Cascade Dynamics
Yaofeng Desmond Zhong, Naomi Ehrich Leonard
We present a continuous threshold model (CTM) of cascade dynamics for a network of agents with real-valued activity levels that change continuously in time. The model generalizes t…
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system, given by an ordinary differential equation (O…