26 citations · 49 across the 11 of their papers we have counts for
11 papers
Symbolic Machine Learning for Vapor-Liquid Equilibrium Prediction in Cx-N2 Binary Mixtures
Bongseok Kim, Suman Chakraborty, Gary Huang +3
Accurate prediction of vapor--liquid equilibrium (VLE) for hydrocarbon-nitrogen mixtures remains challenging for cubic equations of state, particularly across broad ranges of compo…
Energy-Dissipative Evolutionary Deep Operator Neural Networks
Jiahao Zhang, Shiheng Zhang, Jie Shen +1
Energy-Dissipative Evolutionary Deep Operator Neural Network is an operator learning neural network. It is designed to seed numerical solutions for a class of partial differential…
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
Binghang Lu, Christian B. Moya, Guang Lin
This paper presents NSGA-PINN, a multi-objective optimization framework for effective training of Physics-Informed Neural Networks (PINNs). The proposed framework uses the Non-domi…
DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems
Yixuan Sun, Christian Moya, Guang Lin +1
This paper develops a Deep Graph Operator Network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e.g. the power grid or traffic) with an und…
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems
Jiahao Zhang, Shiqi Zhang, Guang Lin
A new data-driven method for operator learning of stochastic differential equations(SDE) is proposed in this paper. The central goal is to solve forward and inverse stochastic prob…
Deformation Robust Roto-Scale-Translation Equivariant CNNs
Liyao Gao, Guang Lin, Wei Zhu
Incorporating group symmetry directly into the learning process has proved to be an effective guideline for model design. By producing features that are guaranteed to transform cov…
Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs
Guang Lin, Christian Moya, Zecheng Zhang
The Deep Operator Networks~(DeepONet) is a fundamentally different class of neural networks that we train to approximate nonlinear operators, including the solution operator of par…
NH-PINN: Neural homogenization based physics-informed neural network for multiscale problems
Wing Tat Leung, Guang Lin, Zecheng Zhang
Physics-informed neural network (PINN) is a data-driven approach to solve equations. It is successful in many applications; however, the accuracy of the PINN is not satisfactory wh…
Vapor-liquid equilibrium predictions of n-alkane/nitrogen mixtures using neural networks
Suman Chakraborty, Yixuan Sun, Guang Lin +1
Understanding fluid phase behavior in high pressure and high temperature conditions is crucial for developing high-fidelity simulations of chemically reacting flows in liquid-fuele…
Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks
Yixuan Sun, Imad Hanhan, Michael D. Sangid +1
Evaluating the mechanical response of fiber-reinforced composites can be extremely time consuming and expensive. Machine learning (ML) techniques offer a means for faster predictio…
Latent Transformations for Object View Points Synthesis
Sangpil Kim, Nick Winovich, Guang Lin +1
We propose a fully-convolutional conditional generative model, the latent transformation neural network (LTNN), capable of view synthesis using a light-weight neural network suited…