Perspectives on the integration between first-principles and data-driven modeling
Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …
essential if it is desired to simultaneously take advantage of both engineering principles and …
Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey
This survey presents recent research on determining control-theoretic properties and
designing controllers with rigorous guarantees using semidefinite programming and for …
designing controllers with rigorous guarantees using semidefinite programming and for …
Bayesian optimization of high‐entropy alloy compositions for electrocatalytic oxygen reduction
Active, selective and stable catalysts are imperative for sustainable energy conversion, and
engineering materials with such properties are highly desired. High‐entropy alloys (HEAs) …
engineering materials with such properties are highly desired. High‐entropy alloys (HEAs) …
A survey of constrained Gaussian process regression: Approaches and implementation challenges
Gaussian process regression is a popular Bayesian framework for surrogate modeling of
expensive data sources. As part of a broader effort in scientific machine learning, many …
expensive data sources. As part of a broader effort in scientific machine learning, many …
Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering
Accurately predicting the remaining useful life (RUL) of lithium-ion rechargeable batteries
remains challenging as the battery capacity degrades in a stochastic manner given the …
remains challenging as the battery capacity degrades in a stochastic manner given the …
Practical and rigorous uncertainty bounds for Gaussian process regression
Gaussian Process regression is a popular nonparametric regression method based on
Bayesian principles that provides uncertainty estimates for its predictions. However, these …
Bayesian principles that provides uncertainty estimates for its predictions. However, these …
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoor positioning and
navigation. By using Maxwell's equations, we derive and present a Bayesian nonparametric …
navigation. By using Maxwell's equations, we derive and present a Bayesian nonparametric …
Learning differentiable solvers for systems with hard constraints
We introduce a practical method to enforce partial differential equation (PDE) constraints for
functions defined by neural networks (NNs), with a high degree of accuracy and up to a …
functions defined by neural networks (NNs), with a high degree of accuracy and up to a …
Gaussian process priors for systems of linear partial differential equations with constant coefficients
M Harkonen, M Lange-Hegermann… - … on machine learning, 2023 - proceedings.mlr.press
Partial differential equations (PDEs) are important tools to model physical systems and
including them into machine learning models is an important way of incorporating physical …
including them into machine learning models is an important way of incorporating physical …
Constraining Gaussian processes to systems of linear ordinary differential equations
A Besginow… - Advances in Neural …, 2022 - proceedings.neurips.cc
Data in many applications follows systems of Ordinary Differential Equations (ODEs). This
paper presents a novel algorithmic and symbolic construction for covariance functions of …
paper presents a novel algorithmic and symbolic construction for covariance functions of …