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MathWorks Inc optimisation toolbox
Basic study: convergence of the objective function f O 2 for Bayesian <t>optimisation</t> and the original HGO algorithm for four LV geometries (HV A, HV B, HV C, HV D). Horizontal axis: Bayesian optimisation iterations starting from 40 iterations for the initial design. Vertical axis: best value of the objective function f O 2 recorded so far. Black dot and horizontal dashed line: the final value of the objective function f O 2 for the HGO algorithm and the associated number of iterations. Bayesian optimisation with a target surrogate (target) and a partial error surrogate (partial) together with the old version of the HGO algorithm (HGO old)
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Basic study: convergence of the objective function f O 2 for Bayesian optimisation and the original HGO algorithm for four LV geometries (HV A, HV B, HV C, HV D). Horizontal axis: Bayesian optimisation iterations starting from 40 iterations for the initial design. Vertical axis: best value of the objective function f O 2 recorded so far. Black dot and horizontal dashed line: the final value of the objective function f O 2 for the HGO algorithm and the associated number of iterations. Bayesian optimisation with a target surrogate (target) and a partial error surrogate (partial) together with the old version of the HGO algorithm (HGO old)

Journal: International Journal for Numerical Methods in Biomedical Engineering

Article Title: Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle

doi: 10.1002/cnm.3593

Figure Lengend Snippet: Basic study: convergence of the objective function f O 2 for Bayesian optimisation and the original HGO algorithm for four LV geometries (HV A, HV B, HV C, HV D). Horizontal axis: Bayesian optimisation iterations starting from 40 iterations for the initial design. Vertical axis: best value of the objective function f O 2 recorded so far. Black dot and horizontal dashed line: the final value of the objective function f O 2 for the HGO algorithm and the associated number of iterations. Bayesian optimisation with a target surrogate (target) and a partial error surrogate (partial) together with the old version of the HGO algorithm (HGO old)

Article Snippet: Specifically, we consider the SQP algorithm (sequential quadratic programming), which is a state‐of‐the art gradient‐based numerical optimiser, implemented in the fmincon function from MATLAB's Optimisation Toolbox.

Techniques:

Basic study: convergence of the objective function for Bayesian optimisation and the original HGO algorithm for four LV geometries (HV A, HV B, HV C, HV D)

Journal: International Journal for Numerical Methods in Biomedical Engineering

Article Title: Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle

doi: 10.1002/cnm.3593

Figure Lengend Snippet: Basic study: convergence of the objective function for Bayesian optimisation and the original HGO algorithm for four LV geometries (HV A, HV B, HV C, HV D)

Article Snippet: Specifically, we consider the SQP algorithm (sequential quadratic programming), which is a state‐of‐the art gradient‐based numerical optimiser, implemented in the fmincon function from MATLAB's Optimisation Toolbox.

Techniques:

Basic study: stretch‐stress curves for four LV geometries (HV A, HV B, HV C, HV D). Left: responses to stretches along the myocyte direction f 0 , right: responses to stretches along the sheet direction s 0 (see (2)). Bayesian optimisation with a target surrogate (target) and a partial error surrogate (partial) together with the old version of the HGO algorithm (HGO old)

Journal: International Journal for Numerical Methods in Biomedical Engineering

Article Title: Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle

doi: 10.1002/cnm.3593

Figure Lengend Snippet: Basic study: stretch‐stress curves for four LV geometries (HV A, HV B, HV C, HV D). Left: responses to stretches along the myocyte direction f 0 , right: responses to stretches along the sheet direction s 0 (see (2)). Bayesian optimisation with a target surrogate (target) and a partial error surrogate (partial) together with the old version of the HGO algorithm (HGO old)

Article Snippet: Specifically, we consider the SQP algorithm (sequential quadratic programming), which is a state‐of‐the art gradient‐based numerical optimiser, implemented in the fmincon function from MATLAB's Optimisation Toolbox.

Techniques:

Klotz‐curve study: convergence of the objective function f O 2 , Klotz for Bayesian optimisation and the original HGO algorithm for four LV geometries (HV A, HV B, HV C, HV D). Horizontal axis: Bayesian optimisation iterations after 40 iterations for the initial design. Vertical axis: best value of the objective function f O 2 recorded so far. Black dot and horizontal dashed line: the final value of the objective function f O 2 for the HGO algorithm and the associated number of iterations. Bayesian optimisation with a target surrogate (targ.) and a partial error surrogate (part.), three independent runs (v1, v2, v3) in each version, together with the new version of the HGO algorithm (HGO new). For HGO, the Klotz curve error was computed using the forward simulator (not the emulator)

Journal: International Journal for Numerical Methods in Biomedical Engineering

Article Title: Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle

doi: 10.1002/cnm.3593

Figure Lengend Snippet: Klotz‐curve study: convergence of the objective function f O 2 , Klotz for Bayesian optimisation and the original HGO algorithm for four LV geometries (HV A, HV B, HV C, HV D). Horizontal axis: Bayesian optimisation iterations after 40 iterations for the initial design. Vertical axis: best value of the objective function f O 2 recorded so far. Black dot and horizontal dashed line: the final value of the objective function f O 2 for the HGO algorithm and the associated number of iterations. Bayesian optimisation with a target surrogate (targ.) and a partial error surrogate (part.), three independent runs (v1, v2, v3) in each version, together with the new version of the HGO algorithm (HGO new). For HGO, the Klotz curve error was computed using the forward simulator (not the emulator)

Article Snippet: Specifically, we consider the SQP algorithm (sequential quadratic programming), which is a state‐of‐the art gradient‐based numerical optimiser, implemented in the fmincon function from MATLAB's Optimisation Toolbox.

Techniques:

Klotz‐curve study: convergence of the objective function for Bayesian optimisation and the updated HGO algorithm for four LV geometries (HV A, HV B, HV C, HV D)

Journal: International Journal for Numerical Methods in Biomedical Engineering

Article Title: Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle

doi: 10.1002/cnm.3593

Figure Lengend Snippet: Klotz‐curve study: convergence of the objective function for Bayesian optimisation and the updated HGO algorithm for four LV geometries (HV A, HV B, HV C, HV D)

Article Snippet: Specifically, we consider the SQP algorithm (sequential quadratic programming), which is a state‐of‐the art gradient‐based numerical optimiser, implemented in the fmincon function from MATLAB's Optimisation Toolbox.

Techniques:

Klotz‐curve study: stretch‐stress curves for four LV geometries (HV A, HV B, HV C, HV D). Left: responses to stretches along the myocyte direction f 0 , right: responses to stretches along the sheet direction s 0 (see (2)). Bayesian optimisation with a target surrogate (targ.) and a partial error surrogate (part.), three independent runs (v1, v2, v3) in each version

Journal: International Journal for Numerical Methods in Biomedical Engineering

Article Title: Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle

doi: 10.1002/cnm.3593

Figure Lengend Snippet: Klotz‐curve study: stretch‐stress curves for four LV geometries (HV A, HV B, HV C, HV D). Left: responses to stretches along the myocyte direction f 0 , right: responses to stretches along the sheet direction s 0 (see (2)). Bayesian optimisation with a target surrogate (targ.) and a partial error surrogate (part.), three independent runs (v1, v2, v3) in each version

Article Snippet: Specifically, we consider the SQP algorithm (sequential quadratic programming), which is a state‐of‐the art gradient‐based numerical optimiser, implemented in the fmincon function from MATLAB's Optimisation Toolbox.

Techniques:

Basic setting: final optimised values of the eight parameters of the HO law for Bayesian optimisation and the original HGO algorithm (HGO old) for four different LV geometries (HV A, HV B, HV C, HV D), Bayesian optimisation with a target surrogate (targ.) and a partial error surrogate (part.)

Journal: International Journal for Numerical Methods in Biomedical Engineering

Article Title: Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle

doi: 10.1002/cnm.3593

Figure Lengend Snippet: Basic setting: final optimised values of the eight parameters of the HO law for Bayesian optimisation and the original HGO algorithm (HGO old) for four different LV geometries (HV A, HV B, HV C, HV D), Bayesian optimisation with a target surrogate (targ.) and a partial error surrogate (part.)

Article Snippet: Specifically, we consider the SQP algorithm (sequential quadratic programming), which is a state‐of‐the art gradient‐based numerical optimiser, implemented in the fmincon function from MATLAB's Optimisation Toolbox.

Techniques:

Klotz‐curve study: final optimised values of the parameters of the HO law for Bayesian optimisation and the updated HGO algorithm for four LV different geometries (HV A, HV B, HV C, HV D)

Journal: International Journal for Numerical Methods in Biomedical Engineering

Article Title: Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle

doi: 10.1002/cnm.3593

Figure Lengend Snippet: Klotz‐curve study: final optimised values of the parameters of the HO law for Bayesian optimisation and the updated HGO algorithm for four LV different geometries (HV A, HV B, HV C, HV D)

Article Snippet: Specifically, we consider the SQP algorithm (sequential quadratic programming), which is a state‐of‐the art gradient‐based numerical optimiser, implemented in the fmincon function from MATLAB's Optimisation Toolbox.

Techniques:

Klotz study: decomposition of the incumbent trajectories from Figure based on f O 2 , Klotz from (11) into f O 2 from (6) (top) and the Klotz component (bottom). Bayesian optimisation with a target surrogate (targ.) and a partial error surrogate (part.), three independent runs (v1, v2, v3) in each version

Journal: International Journal for Numerical Methods in Biomedical Engineering

Article Title: Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle

doi: 10.1002/cnm.3593

Figure Lengend Snippet: Klotz study: decomposition of the incumbent trajectories from Figure based on f O 2 , Klotz from (11) into f O 2 from (6) (top) and the Klotz component (bottom). Bayesian optimisation with a target surrogate (targ.) and a partial error surrogate (part.), three independent runs (v1, v2, v3) in each version

Article Snippet: Specifically, we consider the SQP algorithm (sequential quadratic programming), which is a state‐of‐the art gradient‐based numerical optimiser, implemented in the fmincon function from MATLAB's Optimisation Toolbox.

Techniques: