research
∙
06/09/2023
RANS-PINN based Simulation Surrogates for Predicting Turbulent Flows
Physics-informed neural networks (PINNs) provide a framework to build su...
share
research
∙
12/12/2022
A Neural ODE Interpretation of Transformer Layers
Transformer layers, which use an alternating pattern of multi-head atten...
research
∙
01/20/2022
Physics-informed neural networks for modeling rate- and temperature-dependent plasticity
This work presents a physics-informed neural network based framework to ...
research
∙
10/30/2021
A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
Emergency vehicles (EMVs) play a critical role in a city's response to t...
research
∙
09/12/2021
EMVLight: A Decentralized Reinforcement Learning Framework for EfficientPassage of Emergency Vehicles
Emergency vehicles (EMVs) play a crucial role in responding to time-crit...
research
∙
02/12/2021
A Differentiable Contact Model to Extend Lagrangian and Hamiltonian Neural Networks for Modeling Hybrid Dynamics
The incorporation of appropriate inductive bias plays a critical role in...
research
∙
12/03/2020
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data
The last few years have witnessed an increased interest in incorporating...
research
∙
11/11/2020
Hamiltonian Q-Learning: Leveraging Importance-sampling for Data Efficient RL
Model-free reinforcement learning (RL), in particular Q-learning is wide...
research
∙
11/03/2020
Frequency-compensated PINNs for Fluid-dynamic Design Problems
Incompressible fluid flow around a cylinder is one of the classical prob...
research
∙
02/20/2020
Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning
In this work, we introduce Dissipative SymODEN, a deep learning architec...
research
∙
10/04/2019
A Conditional Generative Model for Predicting Material Microstructures from Processing Methods
Microstructures of a material form the bridge linking processing conditi...
research
∙
09/26/2019
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learnin...
research
∙
04/03/2019
Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
Classification of jets with deep learning has gained significant attenti...
research
∙
12/01/2017
InverseNet: Solving Inverse Problems with Splitting Networks
We propose a new method that uses deep learning techniques to solve the ...
research
∙
10/23/2017
Convolutional Neural Knowledge Graph Learning
Previous models for learning entity and relationship embeddings of knowl...
research
∙
11/30/2015
Proximal gradient method for huberized support vector machine
The Support Vector Machine (SVM) has been used in a wide variety of clas...
research
∙
11/30/2015
Alternating direction method of multipliers for regularized multiclass support vector machines
The support vector machine (SVM) was originally designed for binary clas...
research
∙
11/16/2014