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Integrating scientific knowledge with machine learning for engineering and environmental systems
J Willard, X Jia, S Xu, M Steinbach, V Kumar - ACM Computing Surveys, 2022 - dl.acm.org
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Kernels for vector-valued functions: A review
MA Alvarez, L Rosasco… - Foundations and Trends …, 2012 - nowpublishers.com
Kernel methods are among the most popular techniques in machine learning. From a
regularization perspective they play a central role in regularization theory as they provide a …
regularization perspective they play a central role in regularization theory as they provide a …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
J Willard, X Jia, S Xu, M Steinbach… - arxiv preprint arxiv …, 2020 - beiyulincs.github.io
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Machine learning of linear differential equations using Gaussian processes
M Raissi, P Perdikaris, GE Karniadakis - Journal of Computational Physics, 2017 - Elsevier
This work leverages recent advances in probabilistic machine learning to discover
governing equations expressed by parametric linear operators. Such equations involve, but …
governing equations expressed by parametric linear operators. Such equations involve, but …
Remarks on multi-output Gaussian process regression
Multi-output regression problems have extensively arisen in modern engineering
community. This article investigates the state-of-the-art multi-output Gaussian processes …
community. This article investigates the state-of-the-art multi-output Gaussian processes …
[PDF][PDF] Computationally efficient convolved multiple output Gaussian processes
MA Alvarez, ND Lawrence - The Journal of Machine Learning Research, 2011 - jmlr.org
Recently there has been an increasing interest in regression methods that deal with multiple
outputs. This has been motivated partly by frameworks like multitask learning, multisensor …
outputs. This has been motivated partly by frameworks like multitask learning, multisensor …
Control functionals for Monte Carlo integration
CJ Oates, M Girolami, N Chopin - Journal of the Royal Statistical …, 2017 - academic.oup.com
A non-parametric extension of control variates is presented. These leverage gradient
information on the sampling density to achieve substantial variance reduction. It is not …
information on the sampling density to achieve substantial variance reduction. It is not …
Estimation of time-varying reproduction numbers underlying epidemiological processes: A new statistical tool for the COVID-19 pandemic
The coronavirus pandemic has rapidly evolved into an unprecedented crisis. The
susceptible-infectious-removed (SIR) model and its variants have been used for modeling …
susceptible-infectious-removed (SIR) model and its variants have been used for modeling …
Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation
Despite the wide implementation of machine learning (ML) technique in traffic flow modeling
recently, those data-driven approaches often fall short of accuracy in the cases with a small …
recently, those data-driven approaches often fall short of accuracy in the cases with a small …
Latent force models
Purely data driven approaches for machine learning present difficulties when data is scarce
relative to the complexity of the model or when the model is forced to extrapolate. On the …
relative to the complexity of the model or when the model is forced to extrapolate. On the …