
Worked on the SwanLab/Swan repository to refactor and simplify key components of a MATLAB-based finite element solver. Focused on improving the right-hand side (RHS) integration by replacing direct Cartesian derivatives with a symmetric shape function derivative method, which clarified numerical pathways and enhanced maintainability. Updated variable naming and loop structures to improve code readability and reduce potential errors, while disabling internal force calculations for linear elasticity in the HyperelasticProblem module to streamline debugging and modeling. Leveraged skills in MATLAB development, finite element analysis, and numerical methods to deliver more predictable solver behavior and lay groundwork for future performance profiling.
May 2025 performance summary for SwanLab/Swan: Focused refactor of RHS computations and simplification of linear elasticity to support debugging and maintainability. Implemented symmetric shape function derivative method for RHS integration, updated variable naming and loop structure for improved accuracy and performance, and disabled linear elasticity internal forces in HyperelasticProblem to simplify modeling. Changes are tracked in MATLAB modules RHSIntegratorShapeSymmDerivative.m (two commits) and HyperelasticProblem_refactoring.m (one commit). Result: clearer numerical paths, easier profiling, reduced debugging complexity, with business value in more predictable solver behavior and faster iteration cycles.
May 2025 performance summary for SwanLab/Swan: Focused refactor of RHS computations and simplification of linear elasticity to support debugging and maintainability. Implemented symmetric shape function derivative method for RHS integration, updated variable naming and loop structure for improved accuracy and performance, and disabled linear elasticity internal forces in HyperelasticProblem to simplify modeling. Changes are tracked in MATLAB modules RHSIntegratorShapeSymmDerivative.m (two commits) and HyperelasticProblem_refactoring.m (one commit). Result: clearer numerical paths, easier profiling, reduced debugging complexity, with business value in more predictable solver behavior and faster iteration cycles.

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