Kailai Xu

According to our database, Kailai Xu authored at least 16 papers between 2010 and 2022.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2022

Physics constrained learning for data-driven inverse modeling from sparse observations.

Kailai Xu, Eric Darve

J. Comput. Phys., 2022

2021

Learning constitutive relations using symmetric positive definite neural networks.

Kailai Xu, Daniel Zhengyu Huang, Eric Darve

J. Comput. Phys., 2021

A general approach to seismic inversion with automatic differentiation.

Weiqiang Zhu, Kailai Xu, Eric Darve, Gregory C. Beroza

Comput. Geosci., 2021

Trust Region Method for Coupled Systems of PDE Solvers and Deep Neural Networks.

Kailai Xu, Eric Darve

CoRR, 2021

ADCME MPI: Distributed Machine Learning for Computational Engineering.

Kailai Xu, Eric Darve

Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021

2020

Learning constitutive relations from indirect observations using deep neural networks.

Daniel Zhengyu Huang, Kailai Xu, Charbel Farhat, Eric Darve

J. Comput. Phys., 2020

ADCME: Learning Spatially-varying Physical Fields using Deep Neural Networks.

Kailai Xu, Eric Darve

CoRR, 2020

Distributed Machine Learning for Computational Engineering using MPI.

Kailai Xu, Weiqiang Zhu, Eric Darve

CoRR, 2020

Solving Inverse Problems in Steady State Navier-Stokes Equations using Deep Neural Networks.

Tiffany Fan, Kailai Xu, Jay Pathak, Eric Darve

CoRR, 2020

Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning.

Kailai Xu, Alexandre M. Tartakovsky, Jeff Burghardt, Eric Darve

CoRR, 2020

Calibrating Multivariate Lévy Processes with Neural Networks.

Kailai Xu, Eric Darve

Proceedings of Mathematical and Scientific Machine Learning, 2020

Data-Driven Inverse Modeling with Incomplete Observations.

Kailai Xu, Eric Darve

Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to, 2020

2019

Learning Hidden Dynamics using Intelligent Automatic Differentiation.

Kailai Xu, Dongzhuo Li, Eric Darve, Jerry M. Harris

CoRR, 2019

Adversarial Numerical Analysis for Inverse Problems.

Kailai Xu, Eric Darve

CoRR, 2019

2018

Calibrating Lévy Process from Observations Based on Neural Networks and Automatic Differentiation with Convergence Proofs.

Kailai Xu, Eric Darve

CoRR, 2018

2010

Study on Stability of Vehicle Mass Analysis System.

Wei Shi, Shusheng Xiong, Chaoshan Zhang, Yaohua Jiang, Wei Li, Xijiang Wu, Xiaoshuai Ren, Wenhua He, Kailai Xu, Ji Zhou

Proceedings of the Information and Automation - International Symposium, 2010