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 …
[HTML][HTML] Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: Application to planar soft tissues
Data-based approaches are promising alternatives to the traditional analytical constitutive
models for solid mechanics. Herein, we propose a Gaussian process (GP) based …
models for solid mechanics. Herein, we propose a Gaussian process (GP) based …
A Gaussian process based method for data-efficient remaining useful life estimation
The task of remaining useful life (RUL) estimation is a major challenge within the field of
prognostics and health management (PHM). The quality of the RUL estimates determines …
prognostics and health management (PHM). The quality of the RUL estimates determines …
Compositional uncertainty in deep Gaussian processes
Gaussian processes (GPs) are nonparametric priors over functions. Fitting a GP implies
computing a posterior distribution of functions consistent with the observed data. Similarly …
computing a posterior distribution of functions consistent with the observed data. Similarly …
Monotonic Gaussian process for physics-constrained machine learning with materials science applications
Physics-constrained machine learning is emerging as an important topic in the field of
machine learning for physics. One of the most significant advantages of incorporating …
machine learning for physics. One of the most significant advantages of incorporating …
A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators
Ensuring vertical separation is a key means of maintaining safe separation between aircraft
in congested airspace. Aircraft trajectories are modelled in the presence of significant …
in congested airspace. Aircraft trajectories are modelled in the presence of significant …
Bayesian analysis of constrained Gaussian processes
H Maatouk, D Rullière, X Bay - Bayesian Analysis, 2024 - projecteuclid.org
Due to their flexibility Gaussian processes are a well-known Bayesian framework for
nonparametric function estimation. Integrating inequality constraints, such as monotonicity …
nonparametric function estimation. Integrating inequality constraints, such as monotonicity …
Neuro-symbolic neurodegenerative disease modeling as probabilistic programmed deep kernels
A Lavin - International Workshop on Health Intelligence, 2021 - Springer
We present a probabilistic programmed deep kernel learning approach to personalized,
predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of …
predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of …
Integrated computational materials engineering with monotonic gaussian processes
Physics-constrained machine learning is emerging as an important topic in the field of
machine learning for physics. One of the most significant advantages of incorporating …
machine learning for physics. One of the most significant advantages of incorporating …
Interactive multi-objective reinforcement learning in multi-armed bandits with gaussian process utility models
In interactive multi-objective reinforcement learning (MORL), an agent has to simultaneously
learn about the environment and the preferences of the user, in order to quickly zoom in on …
learn about the environment and the preferences of the user, in order to quickly zoom in on …