Integral equations are used in science and engineering to model complex systems with non-local dependencies; however, existing traditional and machine-learning-based methods cannot yield accurate or efficient solutions in several complex cases. Zappala and colleagues introduce a neural-network-based method that can learn an integral operator and its dynamics from data, demonstrating higher accuracy or scalability compared with several state-of-the-art methods.
- Emanuele Zappala
- Antonio Henrique de Oliveira Fonseca
- David van Dijk
