The field of Scientific Machine Learning (SciML) holds vast potential for industrial applications. It can enable a shift from innovations constrained by human resource availability to innovations driven by data and computing power. Our mini-symposium, featuring a mix of one-third industry and two-thirds academic presentations, will review both the challenges faced by industries and the opportunities presented by scientific advancements. The mini-symposium will conclude with an interactive panel discussion on the future of SciML, involving speakers from our lineup as well as participants. The goal is to outline future research fields that guide, particularly young, researchers aiming to foster the industrialization of SciML and to stimulate new research collaborations. Main topics will include: ¨ What are the industrial challenges and needs in this field? ¨ Why haven't we seen more SciML applications in industry already? ¨ What is the expected timeline for SciML to become widely adopted in industry? ¨ What are the major technical challenges and opportunities? ¨ Which computational frameworks are most suitable? ¨ How can we best access and share data? ¨ …