In recent years, the area of magnonics has seen an enhancement with various creative devices, made possible through devoted skill and detailed investigation. The implementation of machine learning and inverse design, well-established in photonics and the creation of big-scale integrated CMOS circuits, has now been extended into magnonics. This exciting blend opens up new opportunities for magnonic technology. It is ready for a fast and transformative ascent with advanced computational design methods. The powerful impact of artificial intelligence, which is quickly changing our daily lives, also has great potential to advance the field of magnonics.

The inverse-design approach elegantly prioritizes the definition of desired outcomes, leveraging feedback-driven computational algorithms, such as those found in machine learning, to architect devices that fulfill these specified functions. Emblematic of its versatility, two parallel publications have heralded the success of inverse design in magnonics. Illustrating the method's broad applicability, an array of magnonic features—linear, nonlinear, and nonreciprocal—were explored as documented in [Nature Commun. 12, 2636 (2021)], employing the technique to invent a magnonic (de)multiplexer, a nonlinear switch, and a Y-circulator. This was embodied in a three-port prototype, ingeniously crafted from a ferromagnetic rectangle structured by square voids, guided by a direct binary search algorithm. Furthermore, in [Nature Commun. 12, 6422 (2021)], researchers embraced a higher tier of complexity, inverse-designing a neural network manifested as a YIG domain with a precise arrangement of nanomagnets. This demonstrated that neuromorphic computing tasks, including the intricate signal routing required for nonlinear activations, can be adeptly executed through the manipulation of spin-wave travel and interference—with the intricate task of vowel recognition showcasing the prowess of the inverse-designed magnonic neural network.

Our group, together with the Physics of Functional Materials group, is investigating the concept of inverse design magnonics from different perspectives. First, we have just shown that it is possible to perform inverse design directly in the experiment, and a universal inverse-design magnonic device has been fabricated [Nature Electr. 2025]. The device has been used to implement various high-frequency components and can handle data in the GHz range. In follow-up studies, we have shown that it is also highly efficient in implementing nonlinear operations, including logic gates for binary data processing. In parallel, we develop new approaches [arXiv 2411.19109] to numerically solve inverse problems of arbitrary complexity. Finally, the experimental realisation of nanoscale and non-volatile inverse design data processing units is a high priority for our research.