The integration of experimental and computational methods in biomechanics is critical to support the development of personalized diagnostics and treatments. In silico models serve as powerful tools for hypothesis testing and exploration of scenarios that are difficult or impossible to reproduce experimentally. To ensure clinically meaningful predictions, such models must be carefully calibrated and validated against experimental data. This process is especially critical in medical applications, where model accuracy directly impacts patient outcomes. Calibration requires determining model parameters, many of which cannot be measured directly and must be inferred through inverse analysis methods. Beyond parameter estimation, a key challenge lies in accounting for uncertainties inherent in experimental measurements, model structure, and parameter variability. These uncertainties must be quantified to ensure that predictions are robust and reliable. In the context of biological tissues, the use of advanced statistical tools and carefully designed workflows is mandatory to cope with intrinsic heterogeneities and anisotropies. However, by combining experimental rigor with computational power, researchers can enhance model fidelity, inform targeted experimentation, and expedite the translation of insights into clinical practice. This minisymposium aims to showcase recent advances and applications at the interface of experimental and computational biomechanical modeling. We welcome contributions in theoretical developments, computational methods, experimental data analysis, and applied case studies. Topics of interest include, but are not limited to: - Methods for parameter estimation and inverse problems in biomechanics. - Methods for uncertainty quantification in bioengineering. - Validation frameworks for clinical translation. - Data assimilation and model-informed experimental design. - Case studies integrating experimental and computational approaches.