Dynamic solid-state microwave defrosting strategy with shifting frequency and adaptive power improves thawing performance
Nonuniform microwave heating causes localized over-heating in frozen food products,
lowering the quality of the thawed products. The solid-state microwave system has the …
lowering the quality of the thawed products. The solid-state microwave system has the …
The Gaussian process distribution of relaxation times: A machine learning tool for the analysis and prediction of electrochemical impedance spectroscopy data
Electrochemical impedance spectroscopy (EIS) is one of the most important techniques in
electrochemistry. However, analyzing the EIS data is not a simple task. The distribution of …
electrochemistry. However, analyzing the EIS data is not a simple task. The distribution of …
Transferable force fields from experimental scattering data with machine learning assisted structure refinement
Deriving transferable pair potentials from experimental neutron and X-ray scattering
measurements has been a longstanding challenge in condensed matter physics. State-of …
measurements has been a longstanding challenge in condensed matter physics. State-of …
Accelerated Bayesian Inference for Molecular Simulations using Local Gaussian Process Surrogate Models
While Bayesian inference is the gold standard for uncertainty quantification and
propagation, its use within physical chemistry encounters formidable computational barriers …
propagation, its use within physical chemistry encounters formidable computational barriers …
Marginalising over stationary kernels with Bayesian quadrature
S Hamid, S Schulze, MA Osborne… - arXiv preprint arXiv …, 2021 - arxiv.org
Marginalising over families of Gaussian Process kernels produces flexible model classes
with well-calibrated uncertainty estimates. Existing approaches require likelihood …
with well-calibrated uncertainty estimates. Existing approaches require likelihood …
Stationarity without mean reversion: Improper Gaussian process regression and improper kernels
L Ambrogioni - arXiv preprint arXiv:2310.02877, 2023 - arxiv.org
Gaussian processes (GP) regression has gained substantial popularity in machine learning
applications. The behavior of a GP regression depends on the choice of covariance function …
applications. The behavior of a GP regression depends on the choice of covariance function …
Sparse gaussian processes via parametric families of compactly-supported kernels
J Barber - arXiv preprint arXiv:2006.03673, 2020 - arxiv.org
Gaussian processes are powerful models for probabilistic machine learning, but are limited
in application by their $ O (N^ 3) $ inference complexity. We propose a method for deriving …
in application by their $ O (N^ 3) $ inference complexity. We propose a method for deriving …
Bayesian quadrature for Gaussian process kernel learning, neural ensemble search, and high dimensional integrands
S Hamid - 2023 - ora.ox.ac.uk
The central challenge of performing inference in a model is the computation of
marginalisation integrals over the model's parameters. In most cases of interest, these …
marginalisation integrals over the model's parameters. In most cases of interest, these …
[图书][B] Computational and Experimental Perspectives for the Development of Solid State Ionics for Energy Storage Systems
J Liu - 2021 - search.proquest.com
Energy storage systems, in particular, those based on electrochemical reactions, are
becoming more and more crucial to power an environmentally friendly world with renewable …
becoming more and more crucial to power an environmentally friendly world with renewable …
Towards data-efficient deployment of reinforcement learning systems
S Schulze - 2021 - ora.ox.ac.uk
A fundamental concern in the deployment of artificial agents in real-life is their capacity to
quickly adapt to their surroundings. Traditional reinforcement learning (RL) struggles with …
quickly adapt to their surroundings. Traditional reinforcement learning (RL) struggles with …