Recent advances in computational mechanics have greatly improved our capacity to model, analyse, and predict damage phenomena in engineering materials and structures. These developments are driven by progress in numerical algorithms, high-performance computing, and the integration of data-driven techniques with physics-based formulations. The resulting hybrid methodologies offer enhanced accuracy, robustness, and scalability for simulating complex failure mechanisms under diverse material behaviours and loading scenarios. The purpose of this mini symposium is to provide a forum for discussion of challenges and advances in computational modeling and prognosis of damage, placing special emphasis on efficient, robust, physically informed, and data-driven approaches. We particularly welcome contributions that bridge mechanics with innovations from applied mathematics, materials science, and data science. The topics of interest include, but are not limited to the following: 1. Multi-scale and multi-physics approaches for damage and failure prediction 2. Physics-enhanced and data-driven machine learning techniques for fracture mechanics 3. Structural health monitoring and damage prognosis 4. Inverse problems involving fracture, such as fracture parameter identification 5. Vibration- and impact-induced fracture processes 6. Advanced discretization techniques and solution algorithms 7. Verification, validation, and benchmarking of computational methods We encourage both theoretical developments and application-oriented studies. Of special interest are contributions relevant to domains such as civil, mechanical, aerospace, automotive, and energy engineering.