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Integrating scientific knowledge with machine learning for engineering and environmental systems
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
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
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 …
A differentiable programming system to bridge machine learning and scientific computing
Scientific computing is increasingly incorporating the advancements in machine learning
and the ability to work with large amounts of data. At the same time, machine learning …
and the ability to work with large amounts of data. At the same time, machine learning …
Fast machine-learning online optimization of ultra-cold-atom experiments
We apply an online optimization process based on machine learning to the production of
Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation …
Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation …
[PDF][PDF] Computationally efficient convolved multiple output Gaussian processes
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 …
Sparse convolved Gaussian processes for multi-output regression
We present a sparse approximation approach for dependent output Gaussian processes
(GP). Employing a latent function framework, we apply the convolution process formalism to …
(GP). Employing a latent function framework, we apply the convolution process formalism to …
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 …
Computational inference of gene regulatory networks: approaches, limitations and opportunities
Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae,
the study of gene regulatory networks has led to the discovery of regulatory mechanisms …
the study of gene regulatory networks has led to the discovery of regulatory mechanisms …
Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes
Identification and comparison of nonlinear dynamical systems using noisy and sparse
experimental data is a vital task in many fields, however current methods are …
experimental data is a vital task in many fields, however current methods are …