Nowadays, since more and more powerful heterogeneous computers are continuously emerging, scientists and engineers have been facing unprecedented challenges in adapting their workflows to the challenges posed by digital twins and scientific machine learning. This mini-symposium provides a forum for attendees to exchange information, share best practices, and keep current on the rapidly evolving information technologies impacting the convergence of simulation tools, digital twins, and scientific machine learning. The Mini-Symposium topics cover (but are not limited to): Computational environments for advanced scientific machine learning and engineering computations Digital prototyping techniques Enabling software technologies Data science in computational mechanics applications Software libraries and applications for digital twins, model reduction, and machine learning Supporting tools in performance evaluation, visualization, verification, and validation Scientific workflows, theoretical frameworks, methodology, and algorithms