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ode45 solver  (MathWorks Inc)


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    MathWorks Inc ode45 solver
    Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's <t>ode45</t> solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.
    Ode45 Solver, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/ode45/pmc12175709-191-11-11
    Average 90 stars, based on 1 article reviews
    ode45 solver - by Bioz Stars, 2026-10
    90/100 stars

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    1) Product Images from "Modelling the potential impact of TB-funded prevention programs on the transmission dynamics of TB"

    Article Title: Modelling the potential impact of TB-funded prevention programs on the transmission dynamics of TB

    Journal: Infectious Disease Modelling

    doi: 10.1016/j.idm.2025.05.010

    Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.
    Figure Legend Snippet: Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.

    Techniques Used: Comparison

    Related Articles

    other:

    Article Title: Supplementary Information for Embedded Shape Morphing for Morphologically Adaptive Robots
    Article Snippet: Then the ODEs are integrated from s = 0 to s = L using a standard library code (e.g., ode45 in Matlab), during which the distributed wrench and point wrenches will need to be calculated.

    Article Title: A rate dependent interface model for stick-slip fracture in adhesives and polymer glasses.
    Article Snippet: In stick-slip fracture, a crack stays still or only propagates a small amount until it reaches a critical energy release rate.. Then, it suddenly grows rapidly, causing the energy release rate to drop and the crack to stop again.. This behavior is common in many polymers, including glassy polymers and soft materials like adhesives.

    Article Title: Protocol to quantify energy landscape of high-dimensional oscillatory systems using diffusion decomposition.
    Article Snippet: Run the ODE solver like ‘ode45’ in MATLAB to solve the ODE _x = F(x) from x(0) = x0 until the solution x(t); 0 ≤ t ≤ T̂ (where T̂ is sufficiently large) converges into a periodic trajectory.

    Article Title: Investigating Tumour Responses to Combinations of Radiotherapy and Hyperthermia
    Article Snippet: We use MATLAB’s ODE45, a single step built-in solver for non-stiff ODEs based on an explicit Runge–Kutta (4,5) formula, the Dormand-Prince pair (Dormand and Prince ).

    Article Title: Mathematical model of tumor-macrophage dynamics in glioma to advance myeloid-targeted therapies
    Article Snippet: The simulations corresponding to mice treated with IR were solved numerically in a piecewise manner using MATLAB’s ode45 during continuous intervals without treatment.

    Article Title: Investigating Tumour Responses to Combinations of Radiotherapy and Hyperthermia.
    Article Snippet: We use MATLAB’s ODE45, a single step built-in solver for non-stiff ODEs based on an explicit Runge–Kutta (4,5) formula, the Dormand-Prince pair (Dormand and Prince 1980).

    Article Title: Modelling the impact of vaccination on cholera transmission dynamics under stratified populations and seasonality
    Article Snippet: The model was solved by using the Matlab function ode45 and minimization was achieved with the built-in constraint minimization algorithm ”fminsearchbnd” in Matlab (R2023b).

    Article Title: Evolution of foraging behaviour induces variable complexity-stability relationships in mutualist-exploiter-predator communities.
    Article Snippet: In model simulations (via ODE45 in Matlab R2016a), species and foraging efforts were deemed extinct if the biomass fell below 10-12.



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    Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's <t>ode45</t> solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.
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    Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.

    Journal: Infectious Disease Modelling

    Article Title: Modelling the potential impact of TB-funded prevention programs on the transmission dynamics of TB

    doi: 10.1016/j.idm.2025.05.010

    Figure Lengend Snippet: Model fitting results showing the comparison between the predicted TB incidence (solid curve) and the normalized observed data (dots). The model was calibrated by minimizing the sum of squared errors (SSE) between the model output and the data using a least-squares approach. The system of ordinary differential equations was solved using MATLAB's ode45 solver, and parameter optimization was carried out using MATLAB's fminsearch function based on the Nelder–Mead simplex method.

    Article Snippet: To solve the underlying system of ordinary differential equations, we used MATLAB's ode45 solver, which is suitable for non-stiff systems.

    Techniques: Comparison