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ode solver ode45  (MathWorks Inc)


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    MathWorks Inc ode solver ode45
    Ode Solver Ode45, 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/pmc12269494-54-2-7
    Average 90 stars, based on 1 article reviews
    ode solver ode45 - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: The Regularization Continuation Method for Optimization Problems with Nonlinear Equality Constraints
    Article Snippet: The solution curve x(t) of the ODE (15) can not be efficiently followed by the general ODE method [4, 8, 10, 38] such as backward differentiation formulas (BDFs, the subroutine ode15s.m of the MATLAB R2020a environment [62]).

    Article Title: Shortcut model to evaluate the performance of continuous preferential crystallization for conglomerates forming chiral systems
    Article Snippet: The balance equations for the simpler model variant without back-mixing (Eqs. (30) and (35)) were solved with the boundary conditions given in Eq. (36) using “ODE 15 s” solver available in MATLAB (Mathworks, 2017).

    Article Title: Dynamic modeling of a direct reduced iron shaft furnace to enable pathways towards decarbonized steel production
    Article Snippet: Steel production can be decarbonized by utilizing direct reduced iron (DRI) shaft furnaces that use green hydrogen or a mixture of green hydrogen and natural-gas-derived syngas.. To further the implementation of these steel decarbonization methods, the development of a physics-based dynamic model of a DRI shaft furnace is presented and its applications are shown through transient case studies.. The model is validated with industrial data and has mean average errors of 0.3 wt% for outlet solids composition and 1.2 mol% for outlet gas composition.

    Article Title: Masks As a New Hotspot for Antibiotic Resistance Gene Spread: Reveal the Contribution of Atmospheric Pollutants and Potential Risks
    Article Snippet: Herein, a creation function generated the initial population 167 that complied with the relevant criteria before sending it to the ODE model. 168 Subsequently, the simulated values of N0 and N1 after 8 h exposure were determined 169 using ODE 15s (effective stiff solver) in MATLAB 2018b.

    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 ) = x 0 until the solution x ( t ) , 0 ≤ t ≤ T ˆ (where T ˆ is sufficiently large) converges into a periodic trajectory.

    Article Title: Highly efficient dual photoredox/copper catalyzed atom transfer radical polymerization achieved through mechanism-driven photocatalyst design
    Article Snippet: Kinetic simulation were performed with the ordinary differential equations (ODE) based on the rate law using ODE 15s solver implemented in the Matlab program package (R2024a version).2,31 The photophysical behaviors of S1 and T1 are depicted in Supplementary Fig. 13.

    Western Blot:

    Article Title: Cytomorphic electronic system design of apoptosis pathway with extrinsic and intrinsic perturbations.
    Article Snippet: Integrating biological phenomena with electronic circuits paves the way for innovative bio-inspired Systemon-Chip (SoC) designs.. These SoCs capture the stochasticity, non-linearity, and dynamic behavior of complex biological pathways, offering a more economical, faster, and cruelty-free approach to conducting drug trials.. We draw analogies between chemistry and subthreshold electronics to create fast, scalable, bio-inspired Metal Oxide Semiconductor (MOS)-based SoC design of stochastic apoptosis networks.



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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 <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 <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 <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 <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.

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