Andreas Rupp

My research on numerical methods for partial differential equations is driven by a vast field of applications ranging from water management in soils to the design of 3D-printed objects:

Shape optimization for 3D printing is a rapidly growing field that has the potential to revolutionize the way we design and manufacture products. As additive manufacturing (AM) technologies continue to mature and play an increasingly pivotal role in various industries, the need for optimizing the shapes of 3D-printed objects has become increasingly apparent.

Shape optimization leverages advanced computational algorithms and engineering principles to achieve optimal designs that maximize functionality, minimize material usage, and enhance performance across a wide spectrum of applications. One of the primary motivations for shape optimization in 3D printing is to overcome the limitations of traditional manufacturing processes, which are often wasteful and constrained by the chosen manufacturing method. In contrast, AM builds objects layer by layer, offering unprecedented design freedom.

By harnessing shape optimization techniques, engineers and designers can explore intricate geometries that were previously unattainable. This newfound freedom allows for the creation of lightweight structures with intricate internal lattices, organic shapes inspired by nature, and complex structures that optimize load distribution and performance. For example, in the aerospace industry, optimizing the shape of aircraft components can lead to substantial weight reduction, resulting in significant fuel savings and improved overall efficiency.