[preprint] Lotka-Sharpe Neural Operator for Control of Population PDEs

Populations (in ecology, epidemics, biotechnology, economics, social processes) do not only interact over time but also age over time. It is therefore common to model them as age-structured partial differential equations, where age is the ‘space variable’. Since the models also involve integrals over age, both in the birth process and in the interaction among species, they are in fact integro-partial differential equations (IPDEs) with positive states and inputs, turning them into an extremely challenging control problem.

Spotlight Speaker at AUTOMED & MedRob Symposium

I had the joy of attending AUTOMED & MedRob Symposium organized by imes - Institute of Mechatronic Systems of Leibniz Universität Hannover. Focusing on automation, control, and robotics in medicine, this event was amazing on so many levels:

  • As a Spotlight Speaker in the “Who-is-who” talks and announced as a 𝘙𝘪𝘴𝘪𝘯𝘨 𝘴𝘵𝘢𝘳, I had the opportunity to present myself and my research. ✨
  • As Chair of the IEEE Engineering Medicine and Biology Society - Germany/Austria/Switzerland Chapter, we could contribute as a (small) sponsor and I could catch up with the community.
  • I also co-chaired the session on “Prosthetics & Rehabilitation”. 🦾
  • Another highlight was receiving the “Fastest Reviewer Award”. 🏃

[preprint] Infinite-Dimensional Closed-Loop Inverse Kinematics via Neural Operators

In robotics, 𝘤𝘭𝘰𝘴𝘦𝘥-𝘭𝘰𝘰𝘱 𝘪𝘯𝘷𝘦𝘳𝘴𝘦 𝘬𝘪𝘯𝘦𝘮𝘢𝘵𝘪𝘤𝘴 are an efficient tool to position the end-effector of rigid manipulators in space - but they quickly encounter limits with soft robots, where not all configurations are attainable through control action. And also, what if we want to reason about the 𝘦𝘯𝘵𝘪𝘳𝘦 𝘴𝘰𝘧𝘵 𝘳𝘰𝘣𝘰𝘵 𝘴𝘩𝘢𝘱𝘦 while solving tasks, not just the end-effector? 🐘 🐙

Pagination