Year: 2022
Type: article
Abstract: In computational design and fabrication, neural networks are becoming important surrogates for bulky forward simulations. A long-standing, intertwined question is that of inverse design: how to compute a design that satisfies a desired target performance? Here, we show that the piecewise linear property, very common in everyday neural networks, allows for an inverse design formulation based on mixed-integer linear programming. Our mixed-integer inverse design uncovers globally optimal or near optimal solutions in a principled manner. Furthermore, our method significantly facilitates emerging, but challenging, combinatorial inverse design tasks, such as material selection. For problems where finding the optimal solution is intractable, we develop an efficient yet near-optimal hybrid approach. Eventually, our method is able to find solutions provably robust to possible fabrication perturbations among multiple designs with similar performances. Our code and data are available at https://gitlab.mpi-klsb.mpg.de/nansari/mixed-integer-neural-inverse-design.
Source: ACM Transactions on Graphics
Authors: Navid Ansari, Hans-Peter Seidel, Vahid Babaei
Institution: Max Planck Institute for Informatics
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3203363845