In physics and other disciplines, future experimental setups will be so complex that it will be unfeasible for humans to find an optimal set of design parameters. We parameterize the full design of an experiment in a differentiable way and introduce a definition of optimality based on a loss function that encodes the end goals of the experiment. Crucially, we also account for construction constraints, as well as budget, resulting in a constrained optimization problem that we solve using gradient descent.
In this seminar, I will describe the goals and activities of the MODE Collaboration, describing recent efforts such as the optimization of a muon tomography experiment, the optimization of the SWGO experiment configuration, as well as on work and techniques for the differentiability of generation and simulation software, and on perspectives for the scalability of these optimisations via neuromorphic hardware.
