Fractional-order derivatives (FODs) have attracted considerable attention due to their intrinsic nonlocal nature, which enables them to capture memory and hereditary effects more accurately than ...

Physics-informed neural networks (PINNs) have enabled significant improvements in modelling physical processes described by partial differential equations (PDEs) and are in principle capable of ...

Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster. In high ...