135 citations · 272 across the 18 of their papers we have counts for
19 papers
Demonstration Calibrated Port-Hamiltonian Retuning for Manipulation Policies
Yulong Yang, Fan Wu, Christine Allen-Blanchette +1
Diffusion and VLA policies for manipulation are often deployed through downstream impedance controllers. The stiffness and damping gains of these controllers affect task success, y…
Multilevel Graph Wavelet Compressed Sensing with Scale-Aware Neural Recovery
Amirhossein Nouranizadeh, Sarang Rajendra Patil, Alan John Varghese +3
Scientific machine learning methods such as neural operators and physics-informed neural networks have advanced engineering applications and inverse problems, but their training ty…
Using Parametric PINNs for Predicting Internal and External Turbulent Flows
Shinjan Ghosh, Amit Chakraborty, Georgia Olympia Brikis +1
Computational fluid dynamics (CFD) solvers employing two-equation eddy viscosity models are the industry standard for simulating turbulent flows using the Reynolds-averaged Navier-…
An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific Simulations
Valentin Duruisseaux, Amit Chakraborty
In numerous contexts, high-resolution solutions to partial differential equations are required to capture faithfully essential dynamics which occur at small spatiotemporal scales,…
RANS-PINN based Simulation Surrogates for Predicting Turbulent Flows
Shinjan Ghosh, Amit Chakraborty, Georgia Olympia Brikis +1
Physics-informed neural networks (PINNs) provide a framework to build surrogate models for dynamical systems governed by differential equations. During the learning process, PINNs…
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…
Demystifying the Data Need of ML-surrogates for CFD Simulations
Tongtao Zhang, Biswadip Dey, Krishna Veeraraghavan +2
Computational fluid dynamics (CFD) simulations, a critical tool in various engineering applications, often require significant time and compute power to predict flow properties. Th…
Physics-informed neural networks for modeling rate- and temperature-dependent plasticity
Rajat Arora, Pratik Kakkar, Biswadip Dey +1
This work presents a physics-informed neural network (PINN) based framework to model the strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic so…
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…
Frequency-compensated PINNs for Fluid-dynamic Design Problems
Tongtao Zhang, Biswadip Dey, Pratik Kakkar +2
Incompressible fluid flow around a cylinder is one of the classical problems in fluid-dynamics with strong relevance with many real-world engineering problems, for example, design…
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension
Udari Madhushani, Biswadip Dey, Naomi Ehrich Leonard +1
Value function based reinforcement learning (RL) algorithms, for example, -learning, learn optimal policies from datasets of actions, rewards, and state transitions. However, wh…
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 Conditional Generative Model for Predicting Material Microstructures from Processing Methods
Akshay Iyer, Biswadip Dey, Arindam Dasgupta +2
Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering appli…
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…
InverseNet: Solving Inverse Problems with Splitting Networks
Kai Fan, Qi Wei, Wenlin Wang +2
We propose a new method that uses deep learning techniques to solve the inverse problems. The inverse problem is cast in the form of learning an end-to-end mapping from observed da…
Convolutional Neural Knowledge Graph Learning
Feipeng Zhao, Martin Renqiang Min, Chen Shen +1
Previous models for learning entity and relationship embeddings of knowledge graphs such as TransE, TransH, and TransR aim to explore new links based on learned representations. Ho…