Comparing MATLAB Numerical Computation Results with COMSOL Simulation Results

When comparing MATLAB's numerical computation results with COMSOL simulation results, the key lies in understanding the output differences between the two tools and efficiently handling data files.

MATLAB typically outputs numerical results directly through scripts or functions, supporting flexible data formats including matrices, tables, or structures. In code implementation, results can be stored using functions like writematrix() or save() for binary (.mat) files. Conversely, COMSOL simulation results are usually saved in proprietary formats requiring conversion through COMSOL's API or export features to universal formats like CSV or TXT for MATLAB integration. The mphload() function in COMSOL LiveLink for MATLAB can facilitate direct data transfer.

For file operations, MATLAB provides robust I/O functions such as readtable(), csvread(), or importdata() to load and parse COMSOL-exported data. COMSOL supports exporting results to MATLAB-readable formats through its Export node feature. For large datasets, batch reading using MATLAB's datastore() function or data compression in COMSOL can reduce transfer time. Algorithmically, implementing memory-mapped files via memmapfile() enables efficient handling of oversized data.

During data processing, the focus should be on aligning computational conditions (e.g., mesh resolution, boundary conditions) between both tools to ensure consistent comparison baselines. MATLAB's error analysis functions like rmse() for root mean square error or corrcoef() for correlation coefficients can quantify discrepancies. If significant deviations are detected, verify model settings (e.g., solver tolerances in COMSOL versus MATLAB's ODE solver options) and numerical algorithm suitability (e.g., finite element method in COMSOL versus finite difference method in MATLAB) to ensure simulation-theoretical consistency.

Through systematic file operations and data processing techniques, researchers can effectively validate numerical model accuracy, optimize simulation parameters, and enhance research efficiency.